Formation and calibration of nanopore sequencing cells

ABSTRACT

Improved multi-cell nanopore-based sequencing chips and methods can employ formation, characterization, calibration, and/or normalization techniques. For example, various methods may include one or more steps of performing physical checks of cell circuitry, forming and characterizing a lipid layer on the cells, performing a zero point calibration of the cells, forming and characterizing nanopores on the lipid layers of each cell, performing a sequencing operation to accumulate sequencing signals from the cells, normalizing those sequencing signals, and determining bases based on the normalized sequencing signals.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/384,744 filed Apr. 15, 2019, which is a continuation of U.S. patentapplication Ser. No. 15/632,190 filed Jun. 23, 2017, now U.S. Pat. No.10,317,392 issued Jun. 11, 2019, which claims priority to U.S.Provisional Patent Application 62/354,074 filed Jun. 23, 2016, thedisclosures of which are incorporated by reference in their entiretiesfor all purposes.

BACKGROUND

Nanopore membrane devices having pore sizes on the order of onenanometer in internal diameter have shown promise in rapid nucleotidesequencing. When a voltage potential is applied across a nanoporeimmersed in a conducting fluid, a small ion current attributed to theconduction of ions across the nanopore can exist. The size of thecurrent is sensitive to the pore size and which molecule in thenanopore. The molecule can be a particular tag attached to a particularnucleotide, thereby allowing detection of a nucleotide at a particularposition of a nucleic acid. A voltage in a circuit including thenanopore can be measured (e.g., at an integrating capacitor) as a way ofmeasuring the resistance of the molecule, thereby allowing detection ofwhich molecule is in the nanopore.

A nanopore based sequencing chip may be used for DNA sequencing. Ananopore based sequencing chip can incorporate a large number of sensorcells configured as an array. For example, an array of one million cellsmay include 1000 rows by 1000 columns of cells.

The voltages that are measured can vary from chip to chip and from cellto cell of a same chip due to manufacturing variability. Therefore, itcan be difficult to determine the correct molecule, which may be orcorrespond to the correct nucleotide in a particular nucleic acid orother polymer in a cell. In addition, other time dependentnon-idealities in the measured voltages can lead to inaccuracies. And,because these circuits employ biochemical circuit elements, e.g., lipidbilayers, nanopores, etc., the variability in the electricalcharacteristics can be much higher than for traditional semiconductorcircuits.

Accordingly, improved formation, characterization, calibration, andnormalization techniques are desired to improve the accuracy andstability of sequencing processes.

BRIEF SUMMARY

Various embodiments provide techniques and systems related to theformation and calibration of nanopore sequencing cells and also to thenormalization of output signals from cells of a multi-cellnanopore-based sequencing chip.

An improved multi-cell nanopore-based sequencing chip may be built byemploying various embodiment for the formation, characterization,calibration, and/or normalization techniques disclosed herein. Forexample, embodiments can include performing physical checks of cellcircuitry, forming and characterizing a lipid layer on the cells,performing a zero point calibration of the cells, forming andcharacterizing nanopores on the lipid layers of each cell, performing asequencing operation to accumulate sequencing signals from the cells,normalizing those sequencing signals, and determining bases based on thenormalized sequencing signals.

According to one embodiment, the number of nanopores present in eachcell can be characterized. For example, a diagnostic voltage level canbe measured and monitored for each cell. Processing techniques disclosedherein provide for one or more ways to characterize the state of thelipid bilayer (e.g., how many nanopores are present in the bilayer, ifany) based on the measured voltage level. The cells of a sequencing chipwith undesirable pore configuration can be removed or modified, therebyresulting in improved base detection capabilities, e.g., by removingerrors and/or spurious signals produced by cells with undesirable poreconfiguration.

According to another embodiment, cells of the multi-cell nanopore-basedsequencing chip may be calibrated to provide consistent output voltages.For example, cell-specific voltage offsets (also referred to herein aszero point voltages) can be measured and compensated. Measurements canbe made on a one-time basis or may be made multiple times over the lifeof a cell as properties of the cell change over time.

According to another embodiment, methods and systems are directed to thenormalization of sequencing signals that are output from cells of amulti-cell nanopore-based sequencing chip. For example, each cell may bemodelled as a certain type of analog circuit, with the cell's elements(e.g., electrodes, lipid bilayer, nanopore, etc.) being included in themodel as discrete elements, e.g., as resistive and/or capacitiveelements. The model may then be employed to estimate or predict one ormore normalization factors that can be used to correct for a number ofcell-specific non-idealities in a cell's respective sequencing signal,e.g., gain drift and offset shift of sequencing signals may becompensated for using one or more methods disclosed herein. As anotherexample, voltage measurements from one portion of an alternating signal(e.g., a positive voltage relative to a reference voltage) can be usedto normalize voltage measurements made during the opposite portion,e.g., when the nanopores are in threaded states and minimal open channelvoltages are available.

Other embodiments are directed to systems and computer readable mediaassociated with methods described herein.

A better understanding of the nature and advantages of embodiments ofthe present invention may be gained with reference to the followingdetailed description and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a top view of an embodiment of a nanopore sensor chip havingan array of nanopore cells.

FIG. 2 illustrates an embodiment of a nanopore cell in a nanopore sensorchip that can be used to characterize a polynucleotide or a polypeptide.

FIG. 3 illustrates an embodiment of a nanopore cell performingnucleotide sequencing using a nanopore-based sequencing-by-synthesis(Nano-SBS) technique.

FIG. 4 illustrates an embodiment of an electric circuit in a nanoporecell.

FIG. 5 shows example data points captured from a nanopore cell duringbright periods and dark periods of AC cycles.

FIG. 6 shows a flow chart illustrating an example method of formationand calibration of nanopore sequencing cells according to certainembodiments.

FIG. 7 shows a flow chart illustrating an example method of calibrationof nanopore sequencing cells for a sequencing chip according to certainembodiments.

FIG. 8 shows a flow chart illustrating an example method ofcharacterizing the number of nanopores in the cells of a sequencing chipaccording to certain embodiments.

FIGS. 9A-9C show sample open channel voltage data for different statesof the cell according to certain embodiments.

FIG. 10 shows sample histogram data according to certain embodiments.

FIG. 11 shows a kernel density estimate (KDE) of voltage values fordifferent cells according to certain embodiments.

FIGS. 12A and 12B illustrates normalization of sequencing data,according to certain aspects of the present disclosure.

FIG. 13 illustrates gain drift according to certain aspects of thepresent disclosure.

FIG. 14 illustrates baseline shift according to certain aspects of thepresent disclosure

FIG. 15 shows sample data showing both gain drift and baseline shiftaccording to certain aspects of the present disclosure.

FIG. 16 is a flow chart illustrating an example method of normalization,according to certain aspects of the present disclosure.

FIG. 17 shows sample data showing both gain drift and baseline shiftaccording to certain aspects of the present disclosure.

FIG. 18 is a flow chart illustrating an example method of normalization,according to certain aspects of the present disclosure.

FIG. 19 is a flow chart illustrating an example method of normalization,according to certain aspects of the present disclosure.

FIG. 20 is a computer system, according to certain aspects of thepresent disclosure.

TERMS

Unless defined otherwise, technical and scientific terms used hereinhave the same meaning as commonly understood by a person of ordinaryskill in the art. Methods, devices, and materials similar or equivalentto those described herein can be used in the practice of disclosedtechniques. The following terms are provided to facilitate understandingof certain terms used frequently and are not meant to limit the scope ofthe present disclosure. Abbreviations used herein have theirconventional meaning within the chemical and biological arts.

“Nucleic acid” may refer to deoxyribonucleotides or ribonucleotides andpolymers thereof in either single- or double-stranded form. The term mayencompass nucleic acids containing known nucleotide analogs or modifiedbackbone residues or linkages, which are synthetic, naturally occurring,and non-naturally occurring, which have similar binding properties asthe reference nucleic acid, and which are metabolized in a mannersimilar to the reference nucleotides. Examples of such analogs mayinclude, without limitation, phosphorothioates, phosphoramidites, methylphosphonates, chiral-methyl phosphonates, 2-O-methyl ribonucleotides,peptide-nucleic acids (PNAs). Unless otherwise indicated, a particularnucleic acid sequence also implicitly encompasses conservativelymodified variants thereof (e.g., degenerate codon substitutions) andcomplementary sequences, as well as the sequence explicitly indicated.Specifically, degenerate codon substitutions may be achieved bygenerating sequences in which the third position of one or more selected(or all) codons is substituted with mixed-base and/or deoxyinosineresidues (Batzer et al., Nucleic Acid Res. 19:5081 (1991); Ohtsuka etal., J. Biol. Chem. 260:2605-2608 (1985); Rossolini et al., Mol. Cell.Probes 8:91-98 (1994)). The term nucleic acid may be usedinterchangeably with gene, cDNA, mRNA, oligonucleotide, andpolynucleotide.

The term “template” may refer to a single stranded nucleic acid moleculethat is copied into a complementary strand of DNA nucleotides for DNAsynthesis. In some cases, a template may refer to the sequence of DNAthat is copied during the synthesis of mRNA.

The term “primer” may refer to a short nucleic acid sequence thatprovides a starting point for DNA synthesis. Enzymes that catalyze theDNA synthesis, such as DNA polymerases, can add new nucleotides to aprimer for DNA replication.

“Polymerase” refers to an enzyme that performs template-directedsynthesis of polynucleotides. The term encompasses both a full lengthpolypeptide and a domain that has polymerase activity. DNA polymerasesare well-known to those skilled in the art, and include but are notlimited to DNA polymerases isolated or derived from Pyrococcus furiosus,Thermococcus litoralis, and Thermotoga maritime, or modified versionsthereof. They include both DNA-dependent polymerases and RNA-dependentpolymerases such as reverse transcriptase. At least five families ofDNA-dependent DNA polymerases are known, although most fall intofamilies A, B and C. There is little or no sequence similarity among thevarious families. Most family A polymerases are single chain proteinsthat can contain multiple enzymatic functions including polymerase, 3′to 5′ exonuclease activity and 5′ to 3′ exonuclease activity. Family Bpolymerases typically have a single catalytic domain with polymerase and3′ to 5′ exonuclease activity, as well as accessory factors. Family Cpolymerases are typically multi-subunit proteins with polymerizing and3′ to 5′ exonuclease activity. In E. coli, three types of DNApolymerases have been found, DNA polymerases I (family A), II (familyB), and III (family C). In eukaryotic cells, three different family Bpolymerases, DNA polymerases α, δ, and ε, are implicated in nuclearreplication, and a family A polymerase, polymerase γ, is used formitochondrial DNA replication. Other types of DNA polymerases includephage polymerases. Similarly, RNA polymerases typically includeeukaryotic RNA polymerases I, II, and III, and bacterial RNA polymerasesas well as phage and viral polymerases. RNA polymerases can beDNA-dependent and RNA-dependent.

“Nanopore” refers to a pore, channel or passage formed or otherwiseprovided in a membrane. A membrane can be an organic membrane, such as alipid bilayer, or a synthetic membrane, such as a membrane formed of apolymeric material. The nanopore can be disposed adjacent or inproximity to a sensing circuit or an electrode coupled to a sensingcircuit, such as, for example, a complementary metal oxide semiconductor(CMOS) or field effect transistor (FET) circuit. In some examples, ananopore has a characteristic width or diameter on the order of 0.1nanometers (nm) to about 1000 nm. Some nanopores are proteins.

“Nucleotide,” in addition to referring to the naturally occurringribonucleotide or deoxyribonucleotide monomers, can be understood torefer to related structural variants thereof, including derivatives andanalogs, that are functionally equivalent with respect to the particularcontext in which the nucleotide is being used (e.g., hybridization to acomplementary base), unless the context clearly indicates otherwise.

“Tag” refers to a detectable moiety that can be atoms or molecules, or acollection of atoms or molecules. A tag can provide an optical,electrochemical, magnetic, or electrostatic (e.g., inductive,capacitive) signature, which signature may be detected with the aid of ananopore. Typically, when a nucleotide is attached to the tag it iscalled a “Tagged Nucleotide.” The tag can be attached to the nucleotidevia the phosphate moiety.

As used herein, the term “bright period” may generally refer to the timeperiod when a tag of a tagged nucleotide is forced into a nanopore by anelectric field applied through an AC signal. The term “dark period” maygenerally refer to the time period when a tag of a tagged nucleotide ispushed out of the nanopore by the electric field applied through the ACsignal. An AC cycle may include the bright period and the dark period.In different embodiments, the polarity of the voltage signal applied toa nanopore cell to put the nanopore cell into the bright period (or thedark period) may be different.

As used herein, the term “signal value” may refer to a value of thesequencing signal output from a sequencing cell. According to certainembodiments, the sequencing signal may be an electrical signal that ismeasured and/or output from a point in a circuit of one or moresequencing cells e.g., the signal value may be (or represent) a voltageor a current. The signal value may represent the results of a directmeasurement of voltage and/or current and/or may represent an indirectmeasurement, e.g., the signal value may be a measured duration of timefor which it takes a voltage or current to reach a specified value. Asignal value may represent any measurable quantity that correlates withthe resistivity of a nanopore and from which the resistivity and/orconductance of the nanopore (threaded and/or unthreaded) may be derived.

DETAILED DESCRIPTION

According to certain embodiments, techniques and systems disclosedherein relate to cell-by-cell determinations of the number of pores thatperforate a lipid bi-layer within each cell, to cell-by-cell calibrationtechniques, and to sequencing signal normalization methods that can beapplied on a cell-by-cell basis to the output signals of individualcells of a multi-cell nanopore-based sequencing chip.

In various embodiments, a data sampling and conversion circuitassociated with nanopore cells in a column can sequentially sample andconvert output voltage signals from each nanopore cell in the column, aspart of identifying a tag (e.g., attached to a nucleotide) andconsequently a nucleotide being incorporated into a nucleic acid. Thestability and accuracy of the output voltage signal from each nanoporecell is of critical importance to the chip's overall ability toaccurately and quickly sequence a DNA molecule under investigation. Anumber of issues can affect the stability and accuracy of the voltagesignal, including the gain of voltage measurements for a cell can driftover time, the baseline voltage of a cell can shift over time, andvarious DC offsets can develop within a cell as stray charge is injectedinto the cell's capacitive elements.

To address these issues, adaptable signal normalization techniques(e.g., hybrid-online normalization) can perform point-by-pointnormalization of measurements, e.g., of a voltage signal, which can bedone as the measurement is being acquired. The normalized voltages canprovide more consistent values for each tag being detected, and thus canbetter distinguish and identify different bases of the DNA molecule.Offline calibration techniques are also disclosed that can provide for away to detect and correct for certain DC offsets in the baseline voltagelevel for individual cells.

In addition, the properties of individual cells can vary cell-by-celldue to manufacturing non-uniformities. While it is preferable to have asingle nanopore per cell, it is possible that during the poration step(i.e., the process of inserting, for example a protein nanoporetransmembrane molecular complexes (PNTMC) into a cell's lipid bilayer),some cells end up having zero pores and some may have more than one.This can make it difficult to interpret the output signals of thesequencing chip, which itself can contain thousands, if not millions ofcells. Some embodiments can characterize the number of pores present incells of a multi-cell nanopore-based sequencing chip, e.g., viahistogram-based measurement techniques.

I. Nanopore Based Sequencing Chip

FIG. 1 is a top view of an embodiment of a nanopore sensor chip 100having an array 140 of nanopore cells 150. Each nanopore cell 150includes a control circuit integrated on a silicon substrate of nanoporesensor chip 100. In some embodiments, side walls 136 may be included inarray 140 to separate groups of nanopore cells 150 so that each groupmay receive a different sample for characterization. Each nanopore cellmay be used to sequence a nucleic acid. In some embodiments, nanoporesensor chip 100 may include a cover plate 130. In some embodiments,nanopore sensor chip 100 may also include a plurality of pins 110 forinterfacing with other circuits, such as a computer processor.

In some embodiments, nanopore sensor chip 100 may include multiple chipsin a same package, such as, for example, a Multi-Chip Module (MCM) orSystem-in-Package (SiP). The chips may include, for example, a memory, aprocessor, a field-programmable gate array (FPGA), anapplication-specific integrated circuit (ASIC), data converters, ahigh-speed I/O interface, etc.

In some embodiments, nanopore sensor chip 100 may be coupled to (e.g.,docked to) a nanochip workstation 120, which may include variouscomponents for carrying out (e.g., automatically carrying out) variousembodiments of the processes disclosed herein, including, for example,analyte delivery mechanisms, such as pipettes for delivering lipidsuspension or other membrane structure suspension, analyte solution,and/or other liquids, suspension or solids, robotic arms, computerprocessor, and/or memory. A plurality of polynucleotides may be detectedon array 140 of nanopore cells 150. In some embodiments, each nanoporecell 150 can be individually addressable.

II. Nanopore Sequencing Cell

Nanopore cells 150 in nanopore sensor chip 100 may be implemented inmany different ways. For example, in some embodiments, tags of differentsizes and/or chemical structures may be attached to differentnucleotides in a nucleic acid molecule to be sequenced. In someembodiments, a complementary strand to a template of the nucleic acidmolecule to be sequenced may be synthesized by hybridizing differentlypolymer-tagged nucleotides with the template. In some implementations,the nucleic acid molecule and the attached tags may both move throughthe nanopore, and an ion current passing through the nanopore mayindicate the nucleotide that is in the nanopore because of theparticular size and/or structure of the tag attached to the nucleotide.In some implementations, only the tags may be moved into the nanopore.There may also be many different ways to detect the different tags inthe nanopores.

A. Nanopore Sequencing Cell Structure

FIG. 2 illustrates an embodiment of an example nanopore cell 200 in ananopore sensor chip, such as nanopore cell 150 in nanopore sensor chip100 of FIG. 1 , that can be used to characterize a polynucleotide or apolypeptide. Nanopore cell 200 may include a well 205 formed ofdielectric layers 201 and 204; a membrane, such as a lipid bilayer 214formed over well 205; and a sample chamber 215 on lipid bilayer 214 andseparated from well 205 by lipid bilayer 214. Well 205 may contain avolume of electrolyte 206, and sample chamber 215 may hold bulkelectrolyte 208 containing a nanopore, e.g., a soluble protein nanoporetransmembrane molecular complexes (PNTMC), and the analyte of interest(e.g., a nucleic acid molecule to be sequenced).

Nanopore cell 200 may include a working electrode 202 at the bottom ofwell 205 and a counter electrode 210 disposed in sample chamber 215. Asignal source 228 may apply a voltage signal between working electrode202 and counter electrode 210. A single nanopore (e.g., a PNTMC) may beinserted into lipid bilayer 214 by an electroporation process caused bythe voltage signal, thereby forming a nanopore 216 in lipid bilayer 214.The individual membranes (e.g., lipid bilayers 214 or other membranestructures) in the array may be neither chemically nor electricallyconnected to each other. Thus, each nanopore cell in the array may be anindependent sequencing machine, producing data unique to the singlepolymer molecule associated with the nanopore that operates on theanalyte of interest and modulates the ionic current through theotherwise impermeable lipid bilayer.

As shown in FIG. 2 , nanopore cell 200 may be formed on a substrate 230,such as a silicon substrate. Dielectric layer 201 may be formed onsubstrate 230. Dielectric material used to form dielectric layer 201 mayinclude, for example, glass, oxides, nitrides, and the like. An electriccircuit 222 for controlling electrical stimulation and for processingthe signal detected from nanopore cell 200 may be formed on substrate230 and/or within dielectric layer 201. For example, a plurality ofpatterned metal layers (e.g., metal 1 to metal 6) may be formed indielectric layer 201, and a plurality of active devices (e.g.,transistors) may be fabricated on substrate 230. In some embodiments,signal source 228 is included as a part of electric circuit 222.Electric circuit 222 may include, for example, amplifiers, integrators,analog-to-digital converters, noise filters, feedback control logic,and/or various other components. Electric circuit 222 may be furthercoupled to a processor 224 that is coupled to a memory 226, whereprocessor 224 can analyze the sequencing data to determine sequences ofthe polymer molecules that have been sequenced in the array.

Working electrode 202 may be formed on dielectric layer 201, and mayform at least a part of the bottom of well 205. In some embodiments,working electrode 202 is a metal electrode. For non-faradaic conduction,working electrode 202 may be made of metals or other materials that areresistant to corrosion and oxidation, such as, for example, platinum,gold, titanium nitride, and graphite. For example, working electrode 202may be a platinum electrode with electroplated platinum. In anotherexample, working electrode 202 may be a titanium nitride (TiN) workingelectrode. Working electrode 202 may be porous, thereby increasing itssurface area and a resulting capacitance associated with workingelectrode 202. Because the working electrode of a nanopore cell may beindependent from the working electrode of another nanopore cell, theworking electrode may be referred to as cell electrode in thisdisclosure.

Dielectric layer 204 may be formed above dielectric layer 201.Dielectric layer 204 forms the walls surrounding well 205. Dielectricmaterial used to form dielectric layer 204 may include, for example,glass, oxide, silicon mononitride (SiN), polyimide, or other suitablehydrophobic insulating material. The top surface of dielectric layer 204may be silanized. The silanization may form a hydrophobic layer 220above the top surface of dielectric layer 204. In some embodiments,hydrophobic layer 220 has a thickness of about 1.5 nanometer (nm).

Well 205 formed by the dielectric layer walls 204 includes volume ofelectrolyte 206 above working electrode 202. Volume of electrolyte 206may be buffered and may include one or more of the following: lithiumchloride (LiCl), sodium chloride (NaCl), potassium chloride (KCl),lithium glutamate, sodium glutamate, potassium glutamate, lithiumacetate, sodium acetate, potassium acetate, calcium chloride (CaCl₂)),strontium chloride (SrCl₂), manganese chloride (MnCl₂), and magnesiumchloride (MgCl₂). In some embodiments, volume of electrolyte 206 has athickness of about three microns (μm).

As also shown in FIG. 2 , a membrane may be formed on top of dielectriclayer 204 and span across well 205. In some embodiments, the membranemay include a lipid monolayer 218 formed on top of hydrophobic layer220. As the membrane reaches the opening of well 205, lipid monolayer208 may transition to lipid bilayer 214 that spans across the opening ofwell 205. The lipid bilayer may comprise or consist of phospholipid, forexample, selected from diphytanoyl-phosphatidylcholine (DPhPC),1,2-diphytanoyl-sn-glycero-3-phosphocholine,1,2-Di-O-Phytanyl-sn-Glycero-3-phosphocholine (DoPhPC),palmitoyl-oleoyl-phosphatidylcholine (POPC),dioleoyl-phosphatidyl-methylester (DOPME),dipalmitoylphosphatidylcholine (DPPC), phosphatidylcholine,phosphatidylethanolamine, phosphatidylserine, phosphatidic acid,phosphatidylinositol, phosphatidylglycerol, sphingomyelin,1,2-di-O-phytanyl-sn-glycerol;1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethyleneglycol)-350];1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethyleneglycol)-550];1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethyleneglycol)-750];1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethyleneglycol)-1000];1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethyleneglycol)-2000]; 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine-N-lactosyl;GM1 Ganglioside, Lysophosphatidylcholine (LPC) or any combinationthereof.

As shown, lipid bilayer 214 is embedded with a single nanopore 216,e.g., formed by a single PNTMC. As described above, nanopore 216 may beformed by inserting a single PNTMC into lipid bilayer 214 byelectroporation. Nanopore 216 may be large enough for passing at least aportion of the analyte of interest and/or small ions (e.g., Na⁺, K⁺,Ca²⁺, Cl⁻) between the two sides of lipid bilayer 214.

Sample chamber 215 is over lipid bilayer 214, and can hold a solution ofthe analyte of interest for characterization. The solution may be anaqueous solution containing bulk electrolyte 208 and buffered to anoptimum ion concentration and maintained at an optimum pH to keep thenanopore 216 open. Nanopore 216 crosses lipid bilayer 214 and providesthe only path for ionic flow from bulk electrolyte 208 to workingelectrode 202. In addition to nanopores (e.g., PNTMCs) and the analyteof interest, bulk electrolyte 208 may further include one or more of thefollowing: lithium chloride (LiCl), sodium chloride (NaCl), potassiumchloride (KCl), lithium glutamate, sodium glutamate, potassiumglutamate, lithium acetate, sodium acetate, potassium acetate, calciumchloride (CaCl₂)), strontium chloride (SrCl₂), Manganese chloride(MnCl₂), and magnesium chloride (MgCl₂).

Counter electrode (CE) 210 may be an electrochemical potential sensor.In some embodiments, counter electrode 210 may be shared between aplurality of nanopore cells, and may therefore be referred to as acommon electrode. In some cases, the common potential and the commonelectrode may be common to all nanopore cells, or at least all nanoporecells within a particular grouping. The common electrode can beconfigured to apply a common potential to the bulk electrolyte 208 incontact with the nanopore 216. Counter electrode 210 and workingelectrode 202 may be coupled to signal source 228 for providingelectrical stimulus (e.g., voltage bias) across lipid bilayer 214, andmay be used for sensing electrical characteristics of lipid bilayer 214(e.g., resistance, capacitance, and ionic current flow). In someembodiments, nanopore cell 200 can also include a reference electrode212.

In some embodiments, various checks can be made during creation of thenanopore cell as part of calibration. Once a nanopore cell is created,further calibration steps can be performed, e.g., to identify nanoporecells that are performing as desired (e.g., one nanopore in the cell).Such calibration checks can include physical checks, voltagecalibration, open channel calibration, and identification of cells witha single nanopore.

B. Detection Signals of Nanopore Sequencing Cell

Nanopore cells in nanopore sensor chip, such as nanopore cells 150 innanopore sensor chip 100, may enable parallel sequencing using a singlemolecule nanopore-based sequencing by synthesis (Nano-SBS) technique.

FIG. 3 illustrates an embodiment of a nanopore cell 300 performingnucleotide sequencing using the Nano-SBS technique. In the Nano-SBStechnique, a template 332 to be sequenced (e.g., a nucleotide acidmolecule or another analyte of interest) and a primer may be introducedinto bulk electrolyte 308 in the sample chamber of nanopore cell 300. Asexamples, template 332 can be circular or linear. A nucleic acid primermay be hybridized to a portion of template 332 to which four differentlypolymer-tagged nucleotides 338 may be added.

In some embodiments, an enzyme (e.g., a polymerase 334, such as a DNApolymerase) may be associated with nanopore 316 for use in thesynthesizing a complementary strand to template 332. For example,polymerase 334 may be covalently attached to nanopore 316. Polymerase334 may catalyze the incorporation of nucleotides 338 onto the primerusing a single stranded nucleic acid molecule as the template.Nucleotides 338 may comprise tag species (“tags”) with the nucleotidebeing one of four different types: A, T, G, or C. When a taggednucleotide is correctly complexed with polymerase 334, the tag may bepulled (loaded) into the nanopore by an electrical force, such as aforce generated in the presence of an electric field generated by avoltage applied across lipid bilayer 314 and/or nanopore 316. The tailof the tag may be positioned in the barrel of nanopore 316. The tag heldin the barrel of nanopore 316 may generate a unique ionic blockadesignal 340 due to the tag's distinct chemical structure and/or size,thereby electronically identifying the added base to which the tagattaches.

As used herein, a “loaded” or “threaded” tag may be one that ispositioned in and/or remains in or near the nanopore for an appreciableamount of time, e.g., 0.1 millisecond (ms) to 10000 ms. In some cases, atag is loaded in the nanopore prior to being released from thenucleotide. In some instances, the probability of a loaded tag passingthrough (and/or being detected by) the nanopore after being releasedupon a nucleotide incorporation event is suitably high, e.g., 90% to99%.

In some embodiments, before polymerase 334 is connected to nanopore 316,the conductance of nanopore 316 may be high, such as, for example, about300 picosiemens (300 pS). As the tag is loaded in the nanopore, a uniqueconductance signal (e.g., signal 340) is generated due to the tag'sdistinct chemical structure and/or size. For example, the conductance ofthe nanopore can be about 60 pS, 80 pS, 100 pS, or 120 pS, eachcorresponding to one of the four types of tagged nucleotides. Thepolymerase may then undergo an isomerization and a transphosphorylationreaction to incorporate the nucleotide into the growing nucleic acidmolecule and release the tag molecule.

In some cases, some of the tagged nucleotides may not match(complementary bases) with a current position of the nucleic acidmolecule (template). The tagged nucleotides that are not base-pairedwith the nucleic acid molecule may also pass through the nanopore. Thesenon-paired nucleotides can be rejected by the polymerase within a timescale that is shorter than the time scale for which correctly pairednucleotides remain associated with the polymerase. Tags bound tonon-paired nucleotides may pass through the nanopore quickly, and bedetected for a short period of time (e.g., less than 10 ms), while tagsbounded to paired nucleotides can be loaded into the nanopore anddetected for a long period of time (e.g., at least 10 ms). Therefore,non-paired nucleotides may be identified by a downstream processor basedat least in part on the time for which the nucleotide is detected in thenanopore.

A conductance (or equivalently the resistance) of the nanopore includingthe loaded (threaded) tag can be measured via a current passing throughthe nanopore, thereby providing an identification of the tag species andthus the nucleotide at the current position. In some embodiments, adirect current (DC) signal can be applied to the nanopore cell (e.g., sothat the direction at which the tag moves through the nanopore is notreversed). However, operating a nanopore sensor for long periods of timeusing a direct current can change the composition of the electrode,unbalance the ion concentrations across the nanopore, and have otherundesirable effects that can affect the lifetime of the nanopore cell.Applying an alternating current (AC) waveform can reduce theelectro-migration to avoid these undesirable effects and have certainadvantages as described below. The nucleic acid sequencing methodsdescribed herein that utilize tagged nucleotides are fully compatiblewith applied AC voltages, and therefore an AC waveform can be used toachieve these advantages.

The ability to re-charge the electrode during the AC detection cycle canbe advantageous when sacrificial electrodes, electrodes that changemolecular character in the current-carrying reactions (e.g., electrodescomprising silver), or electrodes that change molecular character incurrent-carrying reactions are used. An electrode may deplete during adetection cycle when a direct current signal is used. The recharging canprevent the electrode from reaching a depletion limit, such as becomingfully depleted, which can be a problem when the electrodes are small(e.g., when the electrodes are small enough to provide an array ofelectrodes having at least 500 electrodes per square millimeter).Electrode lifetime in some cases scales with, and is at least partlydependent on, the width of the electrode.

Suitable conditions for measuring ionic currents passing through thenanopores are known in the art and examples are provided herein. Themeasurement may be carried out with a voltage applied across themembrane and pore. In some embodiments, the voltage used may range from−400 mV to +400 mV. The voltage used is preferably in a range having alower limit selected from −400 mV, −300 mV, −200 mV, −150 mV, −100 mV,−50 mV, −20 mV, and 0 mV, and an upper limit independently selected from+10 mV, +20 mV, +50 mV, +100 mV, +150 mV, +200 mV, +300 mV, and +400 mV.The voltage used may be more preferably in the range of 100 mV to 240 mVand most preferably in the range of 160 mV to 240 mV. It is possible toincrease discrimination between different nucleotides by a nanoporeusing an increased applied potential. Sequencing nucleic acids using ACwaveforms and tagged nucleotides is described in US Patent PublicationNo. US 2014/0134616 entitled “Nucleic Acid Sequencing Using Tags,” filedon Nov. 6, 2013, which is herein incorporated by reference in itsentirety. In addition to the tagged nucleotides described in US2014/0134616, sequencing can be performed using nucleotide analogs thatlack a sugar or acyclic moiety, e.g., (S)-Glycerol nucleosidetriphosphates (gNTPs) of the five common nucleobases: adenine, cytosine,guanine, uracil, and thymine (Horhota et al., Organic Letters,8:5345-5347 [2006]).

C. Electric Circuit of Nanopore Sequencing Cell

FIG. 4 illustrates an embodiment of an electric circuit 400 (which mayinclude portions of electric circuit 222 in FIG. 2 ) in a nanopore cell,such as nanopore cell 200. As described above, in some embodiments,electric circuit 400 includes a counter electrode 210 that may be sharedbetween a plurality of nanopore cells or all nanopore cells in ananopore sensor chip, and may therefore also be referred to as a commonelectrode. The common electrode can be configured to apply a commonpotential to the bulk electrolyte (e.g., bulk electrolyte 208) incontact with the lipid bilayer (e.g., lipid bilayer 214) in the nanoporecells by connecting to a voltage source V_(LIQ) 420. In someembodiments, an AC non-Faradaic mode may be utilized to modulate voltageV_(LIQ) with an AC signal (e.g., a square wave) and apply it to the bulkelectrolyte in contact with the lipid bilayer in the nanopore cell. Insome embodiments, V_(LIQ) is a square wave with a magnitude of ±200-250mV and a frequency between, for example, 25 and 400 Hz. The bulkelectrolyte between counter electrode 210 and the lipid bilayer (e.g.,lipid bilayer 214) may be modeled by a large capacitor (not shown), suchas, for example, 100 μF or larger.

FIG. 4 also shows an electrical model 422 representing the electricalproperties of a working electrode (e.g., working electrode 202) and thelipid bilayer (e.g., lipid bilayer 214). Electrical model 422 includes acapacitor 426 (C_(Bilayer)) that models a capacitance associated withthe lipid bilayer and a resistor 428 (R_(PORE)) that models a variableresistance associated with the nanopore, which can change based on thepresence of a particular tag in the nanopore. Electrical model 422 alsoincludes a capacitor 424 having a double layer capacitance(C_(Double Layer)) and representing the electrical properties of workingelectrode 202 and well 205. Working electrode 202 may be configured toapply a distinct potential independent from the working electrodes inother nanopore cells.

Pass device 406 is a switch that can be used to connect or disconnectthe lipid bilayer and the working electrode from electric circuit 400.Pass device 406 may be controlled by control line 407 to enable ordisable a voltage stimulus to be applied across the lipid bilayer in thenanopore cell. Before lipids are deposited to form the lipid bilayer,the impedance between the two electrodes may be very low because thewell of the nanopore cell is not sealed, and therefore pass device 406may be kept open to avoid a short-circuit condition. Pass device 406 maybe closed after lipid solvent has been deposited to the nanopore cell toseal the well of the nanopore cell.

Circuitry 400 may further include an on-chip integrating capacitor 408(n_(cap)). Integrating capacitor 408 may be pre-charged by using a resetsignal 403 to close switch 401, such that integrating capacitor 408 isconnected to a voltage source V_(PRE) 405. In some embodiments, voltagesource V_(PRE) 405 provides a constant reference voltage with amagnitude of, for example, 900 mV. When switch 401 is closed,integrating capacitor 408 may be pre-charged to the reference voltagelevel of voltage source V_(PRE) 405.

After integrating capacitor 408 is pre-charged, reset signal 403 may beused to open switch 401 such that integrating capacitor 408 isdisconnected from voltage source V_(PRE) 405. At this point, dependingon the level of voltage source V_(LIQ), the potential of counterelectrode 210 may be at a level higher than the potential of workingelectrode 202 (and integrating capacitor 408), or vice versa. Forexample, during a positive phase of a square wave from voltage sourceV_(LIQ) (e.g., the bright or dark period of the AC voltage source signalcycle), the potential of counter electrode 210 is at a level higher thanthe potential of working electrode 202. During a negative phase of thesquare wave from voltage source V_(LIQ) (e.g., the dark or bright periodof the AC voltage source signal cycle), the potential of counterelectrode 210 is at a level lower than the potential of workingelectrode 202. Thus, in some embodiments, integrating capacitor 408 maybe further charged during the bright period from the pre-charged voltagelevel of voltage source V_(PRE) 405 to a higher level, and dischargedduring the dark period to a lower level, due to the potential differencebetween counter electrode 210 and working electrode 202. In otherembodiments, the charging and discharging may occur in dark periods andbright periods, respectively.

Integrating capacitor 408 may be charged or discharged for a fixedperiod of time, depending on the sampling rate of an analog-to-digitalconverter (ADC) 410, which may be higher than 1 kHz, 5 kHz, 10 kHz, 100kHz, or more. For example, with a sampling rate of 1 kHz, integratingcapacitor 408 may be charged/discharged for a period of about 1 ms, andthen the voltage level may be sampled and converted by ADC 410 at theend of the integration period. A particular voltage level wouldcorrespond to a particular tag species in the nanopore, and thuscorrespond to the nucleotide at a current position on the template.

After being sampled by ADC 410, integrating capacitor 408 may bepre-charged again by using reset signal 403 to close switch 401, suchthat integrating capacitor 408 is connected to voltage source V_(PRE)405 again. The steps of pre-charging integrating capacitor 408, waitingfor a fixed period of time for integrating capacitor 408 to charge ordischarge, and sampling and converting the voltage level of integratingcapacitor by ADC 410 can be repeated in cycles throughout the sequencingprocess.

A digital processor 430 can process the ADC output data, e.g., fornormalization, data buffering, data filtering, data compression, datareduction, event extraction, or assembling ADC output data from thearray of nanopore cells into various data frames. In some embodiments,digital processor 430 can perform further downstream processing, such asbase determination. Digital processor 430 can be implemented as hardware(e.g., in a GPU, FPGA, ASIC, etc.) or as a combination of hardware andsoftware.

Accordingly, the voltage signal applied across the nanopore can be usedto detect particular states of the nanopore. One of the possible statesof the nanopore is an open-channel state when a tag-attachedpolyphosphate is absent from the barrel of the nanopore, also referredto herein as the unthreaded state of the nanopore. Another four possiblestates of the nanopore each correspond to a state when one of the fourdifferent types of tag-attached polyphosphate nucleotides (A, T, G, orC) is held in the barrel of the nanopore. Yet another possible state ofthe nanopore is when the lipid bilayer is ruptured.

When the voltage level on integrating capacitor 408 is measured after afixed period of time, the different states of a nanopore may result inmeasurements of different voltage levels. This is because the rate ofthe voltage decay (decrease by discharging or increase by charging) onintegrating capacitor 408 (i.e., the steepness of the slope of a voltageon integrating capacitor 408 versus time plot) depends on the nanoporeresistance (e.g., the resistance of resistor R_(PORE) 428). Moreparticularly, as the resistance associated with the nanopore indifferent states is different due to the molecules' (tags') distinctchemical structures, different corresponding rates of voltage decay maybe observed and may be used to identify the different states of thenanopore. The voltage decay curve may be an exponential curve with an RCtime constant τ=RC, where R is the resistance associated with thenanopore (i.e., R_(PORE) 428) and C is the capacitance associated withthe membrane (i.e., capacitor 426 (C_(Bilayer))) in parallel with R. Atime constant of the nanopore cell can be, for example, about 200-500ms. The decay curve may not fit exactly to an exponential curve due tothe detailed implementation of the bilayer, but the decay curve may besimilar to an exponential curve and is monotonic, thus allowingdetection of tags.

In some embodiments, the resistance associated with the nanopore in anopen-channel state may be in the range of 100 MOhm to 20 GOhm. In someembodiments, the resistance associated with the nanopore in a statewhere a tag is inside the barrel of the nanopore may be within the rangeof 200 MOhm to 40 GOhm. In other embodiments, integrating capacitor 408may be omitted, as the voltage leading to ADC 410 will still vary due tothe voltage decay in electrical model 422.

The rate of the decay of the voltage on integrating capacitor 408 may bedetermined in different ways. As explained above, the rate of thevoltage decay may be determined by measuring a voltage decay during afixed time interval. For example, the voltage on integrating capacitor408 may be first measured by ADC 410 at time t1, and then the voltage ismeasured again by ADC 410 at time t2. The voltage difference is greaterwhen the slope of the voltage on integrating capacitor 408 versus timecurve is steeper, and the voltage difference is smaller when the slopeof the voltage curve is less steep. Thus, the voltage difference may beused as a metric for determining the rate of the decay of the voltage onintegrating capacitor 408, and thus the state of the nanopore cell.

In other embodiments, the rate of the voltage decay can be determined bymeasuring a time duration that is required for a selected amount ofvoltage decay. For example, the time required for the voltage to drop orincrease from a first voltage level V1 to a second voltage level V2 maybe measured. The time required is less when the slope of the voltage vs.time curve is steeper, and the time required is greater when the slopeof the voltage vs. time curve is less steep. Thus, the measured timerequired may be used as a metric for determining the rate of the decayof the voltage on integrating capacitor n_(cap) 408, and thus the stateof the nanopore cell. One skilled in the art will appreciate the variouscircuits that can be used to measure the resistance of the nanopore,e.g., including current measurement techniques.

In some embodiments, electric circuit 400 may not include a pass device(e.g., pass device 406) and an extra capacitor (e.g., integratingcapacitor 408 (n_(cap))) that are fabricated on-chip, therebyfacilitating the reduction in size of the nanopore-based sequencingchip. Due to the thin nature of the membrane (lipid bilayer), thecapacitance associated with the membrane (e.g., capacitor 426(C_(Bilayer))) alone can suffice to create the required RC time constantwithout the need for additional on-chip capacitance. Therefore,capacitor 426 may be used as the integrating capacitor, and may bepre-charged by the voltage signal V_(PRE) and subsequently be dischargedor charged by the voltage signal V_(LIQ). The elimination of the extracapacitor and the pass device that are otherwise fabricated on-chip inthe electric circuit can significantly reduce the footprint of a singlenanopore cell in the nanopore sequencing chip, thereby facilitating thescaling of the nanopore sequencing chip to include more and more cells(e.g., having millions of cells in a nanopore sequencing chip).

D. Data Sampling in Nanopore Cell

To perform sequencing of a nucleic acid, the voltage level ofintegrating capacitor (e.g., integrating capacitor 408 (n_(cap)) orcapacitor 426 (C_(Bilayer))) can be sampled and converted by the ADC(e.g., ADC 410) while a tagged nucleotide is being added to the nucleicacid. The tag of the nucleotide can be pushed into the barrel of thenanopore by the electric field across the nanopore that is appliedthrough the counter electrode and the working electrode, for example,when the applied voltage is such that V_(LIQ) is lower than V_(PRE).

1. Threading

A threading event is when a tagged nucleotide is attached to thetemplate (e.g., nucleic acid fragment), and the tag goes in and out ofthe barrel of the nanopore. This can happen multiple times during athreading event. When the tag is in the barrel of the nanopore, theresistance of the nanopore may be higher, and a lower current may flowthrough the nanopore.

During sequencing, a tag may not be in the nanopore in some AC cycles(referred to as an open-channel state), where the current is the highestbecause of the lower resistance of the nanopore. When a tag is attractedinto the barrel of the nanopore, the nanopore is in a bright mode. Whenthe tag is pushed out of the barrel of the nanopore, the nanopore is ina dark mode.

2. Bright and Dark Period

During an AC cycle, the voltage on integrating capacitor may be sampledmultiple times by the ADC. For example, in one embodiment, an AC voltagesignal is applied across the system at, e.g., about 100 Hz, and anacquisition rate of the ADC can be about 2000 Hz per cell. Thus, therecan be about 20 data points (voltage measurements) captured per AC cycle(cycle of an AC waveform). Data points corresponding to one cycle of theAC waveform may be referred to as a set. In a set of data points for anAC cycle, there may be a subset captured when, for example, V_(LIQ) islower than V_(PRE), which may correspond to a bright mode (period) wherethe tag is forced into the barrel of the nanopore. Another subset maycorrespond to a dark mode (period) where the tag is pushed out of thebarrel of the nanopore by the applied electric field when, for example,V_(LIQ) is higher than V_(PRE).

3. Measured Voltages

For each data point, when the switch 401 is opened, the voltage at theintegrating capacitor (e.g., integrating capacitor 408 (n_(cap)) orcapacitor 426 (C_(Bilayer))) will change in a decaying manner as aresult of the charging/discharging by V_(LIQ), e.g., as an increase fromV_(PRE) to V_(LIQ) when V_(LIQ) is higher than V_(PRE) or a decreasefrom V_(PRE) to V_(LIQ) when V_(LIQ) is lower than V_(PRE). The finalvoltage values may deviate from V_(LIQ) as the working electrodecharges. The rate of change of the voltage level on the integratingcapacitor may be governed by the value of the resistance of the bilayer,which may include the nanopore, which may in turn include a molecule(e.g., a tag of a tagged nucleotides) in the nanopore. The voltage levelcan be measured at a predetermined time after switch 401 opens.

Switch 401 may operate at the rate of data acquisition. Switch 401 maybe closed for a relatively short time period between two acquisitions ofdata, typically right after a measurement by the ADC. The switch allowsmultiple data points to be collected during each sub-period (bright ordark) of each AC cycle of V_(LIQ). If switch 401 remains open, thevoltage level on the integrating capacitor, and thus the output value ofthe ADC, would fully decay and stay there. Instead, when switch 401 isclosed, the integrating capacitor is precharged again (to V_(PRE)) andbecomes ready for another measurement. Thus, switch 401 allows multipledata points to be collected for each sub-period (bright or dark) of eachAC cycle. Such multiple measurements can allow higher resolution with afixed ADC (e.g. 8-bit to 14-bit due to the greater number ofmeasurements, which may be averaged). The multiple measurements can alsoprovide kinetic information about the molecule threaded into thenanopore. The timing information may allow the determination of how longa threading takes place. This can also be used in helping to determinewhether multiple nucleotides that are added to the nucleic acid strandare being sequenced.

FIG. 5 shows example data points captured from a nanopore cell duringbright periods and dark periods of AC cycles. In FIG. 5 , the change inthe data points is exaggerated for illustration purpose. The voltage(V_(PRE)) applied to the working electrode or the integrating capacitoris at a constant level, such as, for example, 900 mV. A voltage signal510 (V_(LIQ)) applied to the counter electrode of the nanopore cells isan AC signal shown as a rectangular wave, where the duty cycle may beany suitable value, such as less than or equal to 50%, for example,about 40%.

During a bright period 520, voltage signal 510 (V_(LIQ)) applied to thecounter electrode is lower than the voltage V_(PRE) applied to theworking electrode, such that a tag may be forced into the barrel of thenanopore by the electric field caused by the different voltage levelsapplied at the working electrode and the counter electrode (e.g., due tothe charge on the tag and/or flow of the ions). When switch 401 isopened, the voltage at a node before the ADC (e.g., at an integratingcapacitor) will decrease. After a voltage data point is captured (e.g.,after a specified time period), switch 401 may be closed and the voltageat the measurement node will increase back to V_(PRE) again. The processcan repeat to measure multiple voltage data points. In this way,multiple data points may be captured during the bright period.

As shown in FIG. 5 , a first data point 522 (also referred to as firstpoint delta (FPD)) in the bright period after a change in the sign ofthe V_(LIQ) signal may be lower than subsequent data points 524. Thismay be because there is no tag in the nanopore (open channel), and thusit has a low resistance and a high discharge rate. In some instances,first data point 522 may exceed the V_(LIQ) level as shown in FIG. 5 .This may be caused by the capacitance of the bilayer coupling the signalto the on-chip capacitor. Data points 524 may be captured after athreading event has occurred, i.e., a tag is forced into the barrel ofthe nanopore, where the resistance of the nanopore and thus the rate ofdischarging of the integrating capacitor depends on the particular typeof tag that is forced into the barrel of the nanopore. Data points 524may decrease slightly for each measurement due to charge built up atC_(Double Layer) 424, as mentioned below.

During a dark period 530, voltage signal 510 (V_(LIQ)) applied to thecounter electrode is higher than the voltage (V_(PRE)) applied to theworking electrode, such that any tag would be pushed out of the barrelof the nanopore. When switch 401 is opened, the voltage at themeasurement node increases because the voltage level of voltage signal510 (V_(LIQ)) is higher than V_(PRE). After a voltage data point iscaptured (e.g., after a specified time period), switch 401 may be closedand the voltage at the measurement node will decrease back to V_(PRE)again. The process can repeat to measure multiple voltage data points.Thus, multiple data points may be captured during the dark period,including a first point delta 532 and subsequent data points 534. Asdescribed above, during the dark period, any nucleotide tag is pushedout of the nanopore, and thus minimal information about any nucleotidetag is obtained, besides for use in normalization.

FIG. 5 also shows that during bright period 540, even though voltagesignal 510 (V_(LIQ)) applied to the counter electrode is lower than thevoltage (V_(PRE)) applied to the working electrode, no threading eventoccurs (open-channel). Thus, the resistance of the nanopore is low, andthe rate of discharging of the integrating capacitor is high. As aresult, the captured data points, including a first data point 542 andsubsequent data points 544, show low voltage levels.

The voltage measured during a bright or dark period might be expected tobe about the same for each measurement of a constant resistance of thenanopore (e.g., made during a bright mode of a given AC cycle while onetag is in the nanopore), but this may not be the case when charge buildsup at double layer capacitor 424 (C_(Double Layer)). This chargebuild-up can cause the time constant of the nanopore cell to becomelonger. As a result, the voltage level may be shifted, thereby causingthe measured value to decrease for each data point in a cycle. Thus,within a cycle, the data points may change somewhat from data point toanother data point, as shown in FIG. 5 .

4. Determining Bases

For each usable nanopore cell of the nanopore sensor chip, a productionmode can be run to sequence nucleic acids. The ADC output data capturedduring the sequencing can be normalized to provide greater accuracy.Normalization can account for offset effects, such as cycle shape andbaseline shift. After normalization, embodiments can determine clustersof voltages for the threaded channels, where each cluster corresponds toa different tag species, and thus a different nucleotide. The clusterscan be used to determine probabilities of a given voltage correspondingto a given nucleotide. As another example, the clusters can be used todetermine cutoff voltages for discriminating between differentnucleotides (bases).

Further details regarding the sequencing operation can be found in, forexample, U.S. Patent Publication No. 2016/0178577 entitled“Nanopore-Based Sequencing With Varying Voltage Stimulus,” U.S. PatentPublication No. 2016/0178554 entitled “Nanopore-Based Sequencing WithVarying Voltage Stimulus,” U.S. patent application Ser. No. 15/085,700entitled “Non-Destructive Bilayer Monitoring Using Measurement OfBilayer Response To Electrical Stimulus,” and U.S. patent applicationSer. No. 15/085,713 entitled “Electrical Enhancement Of BilayerFormation,” the disclosures of which are incorporated by reference intheir entirety for all purposes.

III. Overview of Calibration and Normalization Pipeline

FIG. 6 is a flow chart illustrating an example method of formation andcalibration of nanopore sequencing cells according to certainembodiments. As part of calibration, various checks can be made duringcreation of the sequencing cell. Once a cell is created, furthercalibration steps can be performed, e.g., to identify sequencing cellsthat are performing as desired (e.g., one nanopore in the cell). Oncethe calibration steps are completed, normalization and sequencing can beperformed.

In step 610, physical checks of a cell's circuitry are performed. Some“dry checks” can be performed before any buffer or lipid solution isapplied, and some “wet checks” can be performed after buffer and orlipid solution is applied. For example, each cell of the sequencing chipmay be checked for an open-circuit or short-circuit state. Furtherdescription of the physical checks according to certain embodiments canbe found below in reference to Section IV(A).

In step 620, a lipid layer is formed over each cell. According tocertain embodiments, the thickness of the lipid layer is monitoredduring the formation process, and various feedback processes may operateto ensure that the eventual state of the lipid layer is that of a lipidbilayer. For example, if after a first iteration of applying a lipidsolution to a cell it is determined that the lipid layer is too thickand not a bilayer, a thinning procedure may be initiated. Furtherdescription of the physical checks associated with the lipid bilayeraccording to certain embodiments can be found below in reference toSection IV(A).

In step 630, a zero point voltage calibration is performed for each cellof the sequencing chip. Due to variations in the electronic propertiesof each cell, each cell can have a different DC offset with zero voltsapplied to the cell. The DC offset is referred to herein as a “zeropoint” voltage and, alternatively, as VMzero. For example, there can bemanufacturing imperfections or variation between the analog circuitry ofdifferent cells in the chip. Thus, the ADC for one cell can have adifferent voltage bias than the ADC for another cell. Embodiments canperform calibration to account for such variation. Further descriptionof the zero point voltage calibration according to certain embodimentscan be found below in reference to Section IV(B).

In step 640, nanopores are added to each cell, and the cells arecharacterized to determine how many nanopores per cell have been added.At this step, if too many (more than one) or too few (zero) nanoporeshave been added to a cell, a feedback process may be initiated to eitheradd or remove nanopore(s) from the cell. According to certainembodiments, cells that are found to have more or less than one nanoporecan be deactivated and not used during the sequencing process. Furtherdescription of the nanopore characterization according to certainembodiments can be found below in reference to Section IV(C) and IV(E).According to certain embodiments, the nanopore characterization methodmay be performed by a digital processor as described above in referenceto FIG. 4 and as described below in reference to FIG. 15 and FIG. 19 .

In step 650, a sequencing operation is performed, thereby generatingoutput signals from the cells, e.g., as described above in reference toFIGS. 3-5 . For example, a tail of a tag may be positioned in the barrelof the nanopore, thereby generating a unique output signal due to thetag's distinct chemical structure and/or size. According to certainembodiments, the output signals may be measured via a current passingthrough the nanopore, thereby providing an identification of the tagspecies and thus the nucleotide at a current position in a nucleic acid.In some embodiments, the current or a voltage may be measured by way ofan integrating capacitor, e.g., as described above in reference to FIGS.4-5 .

In step 660, the output signals (e.g., voltage and/or current signals)are normalized. Part of this normalization process can involve measuringand or inferring (through the use of an analog circuit model of thecell) a bright mode open channel voltage and using that bright mode openchannel voltage as a normalization factor for the output signals.Further description of the normalization process according to certainembodiments can be found below in reference to Section IV(D) and SectionV(A)-(G). According to certain embodiments, the normalization method maybe performed by a digital processor as described above in reference toFIG. 4 and as described below in reference to FIG. 15 and FIG. 19 .

While sequencing, calibration, and normalization are shown here asseparate steps, these steps may be performed together as part of thesequencing operation, i.e., each point or group of points that areacquired during sequencing may be subject to a calibration andnormalization step without departing from the scope of the presentdisclosure.

In step 670, one or more processors may determine bases using thenormalized output signals. As described in Section VI below, embodimentscan determine clusters of voltages for the threaded channels, and usethe clusters to determine cutoff voltages for discriminating betweendifferent bases using the normalized output signals.

According to certain embodiments, the order of the calibration andnormalization checks may be different than that shown in the flow chartof FIG. 6 . For example, according to certain embodiments, thecalibration and normalization step may be performed once, e.g., after aninitial manufacturing processing step is complete (e.g. before formationof the lipid bilayer, after formation of the lipid bilayer, beforeformation of the nanopore, after formation of the nanopore, etc.).According to certain embodiments, calibration may be done many timesover the life of a chip (e.g., at scheduled intervals and/or beforeevery sequencing operation). According to certain embodimentscalibration and normalization may be done in an “online” manner, i.e.for each raw data point acquired or every time a group of raw datapoints is acquired.

IV. Calibration

Calibration of a sequencing chip can be performed before sequencingstarts. The calibration can be performed to ensure that no criticalerrors exist, where such critical errors might prevent sequencing to beperformed in one or more cells. Calibration can also be used to obtaincalibration values (e.g., to determine a zero-point voltage) that areused in measuring values (e.g., voltages or currents) or used inanalyzing measured values to obtain corrected or normalized voltagevalues, which can ultimately be used to determine sequence of a nucleicacid.

A. Physical Checks

A dry check can occur before any buffer (e.g., an electrolyte solution)is flowed into the sequencing chip and before a membrane (e.g., a lipidbilayer) is formed over a well. In a dry check, the electricalcomponents of the sequence chip (e.g., for each sequencing cell) arechecked to confirm they are functioning properly, e.g., a signal with anexpected value (e.g., within a specific range) is received from eachwell. At this point, there should be no connection between theelectrodes (e.g., electrodes 202 and 210), since there is no electrolytesolution in the well or sample chamber. If there is a connection (e.g.,a short) then the measured voltage would be outside of an expected range(e.g., a voltage measurement being the same as the reference voltage),thereby indicating there is something wrong.

In a wet check, buffer is flowed over the surface of the chip. Thischeck can make sure that there is a connection (e.g., a short) betweenthe electrodes. If there is no connection then, that indicates aproblem.

In a lipid (cover) check, a solution can be flowed over the chip. Thesolution can be an organic solvent with the lipid dissolved in it. Atthe end of that process, each well should have a plug of the solvent andlipid. There should be no (or minimal) electrical connection between theelectrodes at this point as the lipid layer would be too thick.

In a thinning procedure, the ADC value can be measured for each cell todetermine cells where the lipid layer is too thick, and the bilayer canbe thinned. U.S. patent application Ser. No. 15/085,713 describes anelectrical lipid-thinning stimulus to thin the lipid layer.

A cell can start with a relatively thick lipid layer, which is thinnedto form a lipid bilayer. After thinning, there can be atwo-molecule-thick lipid bilayer that acts as a membrane to cover thewell. In practice, any water-permeable membrane may be used. On theedges of the lipid bilayer is an annulus, an anchoring ring of solvent.The annulus can act as a reservoir of lipids for the bilayer.

The thickness of the lipid layer can be measured using the first pointdelta (FPD), which corresponds to the difference between the firstmeasured voltage points in the bright mode and dark mode respectively,e.g., FPD is the difference between the high first points shown in FIG.5 . The first point delta is proportional to the capacitance of thebilayer, and the bilayer thicknesses is proportional to capacitance.When the lipid layer is thick (e.g., microns), then the capacitance issmall. By the time the thickness shrinks down to about 4 nm, thecapacitance is something measurable, e.g., on the order of 100femtofarads. A bilayer has a deterministic thickness, based on themolecules used, with some small differences based on how the moleculesof the bilayer are arranged and how much solvent remains. According tocertain embodiments, the thickness of the lipid bilayer may be from 4.2to 4.3 nm.

The capacitance of the bilayer (or other membrane) is proportional tothe lateral area, which depends on how much annulus exists. Thus, theFPD can provide whether the bilayer exists and how close to the edge thebilayer has formed.

In some embodiments, a feedback mechanism in the system can be used tofurther thin the bilayer. To thin the lipid layer, a lateral pressurecan be applied (e.g., flowing buffer at high velocity across top of thelipid layer). As another example, one can turn on the AC signal to applyan AC bias, which can effectively shake the layer back and forth untilit achieves the energetically stable state of the bi-layer. Such aprocedure can remove any local minimum in the formation process of thelipid bilayer.

The feedback can act by measuring the FPD over time and adjusting thefeedback. The cells with a sufficiently small FPD (e.g., below athreshold) can have actions performed to thin that particular cell. Sucha process can continue until at least a specified percentage of cells(e.g., 70%) have a usable bilayer.

B. Voltage Calibration

To calibrate the system for different voltages, a zero-point voltage ofeach cell (also referred to herein as VMzero) can be determined. Forelectronic reasons, each cell can have a different DC offset. Forexample, there can be manufacturing imperfections or variations betweenthe analog circuitry of different cells in the chip. Also, a bias can bebuilt into the system for electrochemical reasons. Due to suchmanufacturing variability, one electrode can be slightly different thananother. This can introduce an offset from cell to cell. In addition,the surface chemistry of the electrodes may make them act as batteries,and thus each cell may have a slightly different potential, which cancontribute to the VMzero for each cell. According to certainembodiments, a net effect is that the measured ADC signal is pushed upor down, depending on the value of VMzero. Embodiments can perform acalibration to account for such variation between cells.

FIG. 7 is a flow chart illustrating an example method 700 of calibrationof nanopore sequencing cells for a sequencing chip according to certainembodiments. Method 700 can be performed at various times, e.g., beforea membrane has been formed, after a membrane has been formed (but beforea pore is inserted), and/or after a pores have been inserted into thecells. This calibration can be performed at multiple times in acalibration process, with different values for VMzero being obtained andused for a given stage.

In step 720, a zero point voltage (also referred to herein as VMzero) isobtained for each cell of the sequencing chip. In some embodiments,VMZero is measured by the ADC with zero voltage applied to the cell(e.g., no pathway for current flow). Such a state of zero appliedvoltage can be achieved in various ways, e.g., by disconnecting theworking electrode and/or the counter electrode or by having bothelectrodes be at a same voltage. In this manner, each ADC may receive adifferent floating voltage. Furthermore, the conversion from the analogvalue to the digital can vary from ADC to ADC. According to certainembodiments, the measured set of VMzeros, one for each cell, can bestored in memory. These stored values can be used to calibrate (i.e.,remove the offset from) each cell, thereby ensuring that the ADCmeasurements of both bright and/or dark period voltages are comparablefrom cell to cell. As described above, the zero point voltage for eachcell can be measured by an ADC, e.g., ADC 410 shown in FIG. 4 .

The sequencing chip may include thousands or even millions of cells, andthus thousands or even millions of zero point voltages can be measured.According to certain embodiments, the zero point voltages may bemeasured and stored in memory before the nanopores are inserted into thelipid bilayers of each cell. In some embodiments, the memory may beintegrated onto the sequencing chip or may be an external memory storethat is operatively connected to the sequencing chip, e.g., such as anyform of computer memory, s described below in reference to FIG. 20 .Alternatively or additionally, the zero point voltages may be measuredafter the nanopores are inserted into the lipid bilayers of each cell.As a further example, the zero point voltages may be measured once foreach chip as part of a characterization or calibration step or may bemeasured multiple times over the lifetime of the chip. For example,VMzero may change over time as the capacitance of the double layercapacitor changes, and thus may be measured before and/or after asequencing run to ensure that the system is calibrated properly. Moredetails about how the double layer capacitance contributes to VMzero isdescribed in more detail below in Section V(D)(2).

In step 730, after the nanopores have been inserted into the lipidbilayers, a sequencing operation may be performed and a plurality ofmeasured voltages may be obtained (e.g., by the ADCs of the sequencingchip). The sequencing may be performed during the application of analternating signal across each cell of the chip. The process ofobtaining voltage data in this manner is described above in reference toFIGS. 3-5 .

In step 740, the obtained voltage values are corrected using the storedVMzero values. For example, according to certain embodiments, adifference between a cell's measured values and its VMzero value can becomputed, e.g., by a digital processor 430 in FIG. 4 . Morespecifically, a set of corrected voltage values can be obtained for eachcell by subtracting that cell's VMzero from the measured voltage values.

Accordingly, a zero point voltage value (e.g., as VMzero) can bedetermined for each cell and used to optimize the dynamic range of theADC. For example, an ADC can provide a specified data range, e.g., an8-bit unsigned range (0 to 255). The difference between the digitalvalues are controlled by the manufacturing of the ADC, but the specificanalog range can be varied (e.g., as controlled by an ADC referencevoltage) to correspond to an expected range of the analog voltage forthe sequencing cells, taking into account the cell-specific VMzero. Thezero value for the ADC need not correspond to zero volts, as therelative voltages is what is used.

In one embodiment, there are two reference voltages that set the bottomand the top of the ADC voltage range. The two voltages can be ofdifferent sign. The reference voltages can be set externally. Thereference voltages can be changed as different biochemistry is used. Theactual signal should be within the reference range, and ideally take upmost of that reference range. According to certain embodiments,knowledge of the measured VMzero for each cell can may be used to setthe reference voltages for each cell independently. This can ensure thatthe full dynamic range of the ADC is being used, thereby minimizingquantization noise.

C. Insertion of Nanopore

Nanopores can be inserted into the lipid bilayer a number of differentways. For example, if relying on force of pressure in the chip torandomly diffuse the pores into the membranes, then the proportion wouldbe governed by binomial distribution. In such a situation, many cellswould have zero nanopores, some would have one, some would have two, andthe majority would not have just one. However, according to certainembodiments, just one nanopore per cell is best for sequencing. If thereare more than one nanopore per cell, e.g., two nanopores per cell, thenthe signal from the pore will be some combination of the two signalsfrom the two pores, which can cause the levels to have error, as such asystem has a different equivalent circuit than a single pore cell.Furthermore, the combined signal would results from tags entering thenanopore at different times, making it difficult to know which base tocall at a given time.

According to certain embodiments, electroporation can be used to insertthe nanopores into the bilayer. Electroporation applies a square waveacross the bilayer to stress it. Too high a voltage would pop the lipidlayer. But, a suitable voltage can provide a tear where the nanopore canbe inserted more easily.

As mentioned above, it is beneficial for each cell to have exactly onenanopore. To accomplish this, according to certain embodiments adiagnostic measurement can be taken for each cell before, during, andafter the electroporation signal is applied, e.g., a voltage value akinto an open channel measurement described above in reference to FIGS. 4-5can be measured. The measured value can then be then analyzed todetermine whether the measured value corresponds to a value that wouldbe expected for a cell having only one nanopore. A single nanopore maybe detected by tracking a voltage changes during the electroporationprocess, and if the voltage changes significantly then it is assumedthat proration has successfully completed.

When a nanopore is observed to have been added to a cell, theelectroporation process can be stopped for that well. This can be doneindependently for each well. As described in further detail below, theabove process can be used in combination with a diagnostic techniquethat employs a voltage histogram/distribution of the open channelvoltages for all the cells across the sequencing chip to identify anopen-channel voltage, or range of open channel voltages, that indicate asingle nanopore cell. For those cells that do not have any pores afterthe first electroporation step, the electroporation may be repeated.

D. Open Channel Calibration

After electroporation, the output voltage of a cell with no tag in placecan be measured to determine the initial voltage of the cell. Asdescribed above in reference to FIGS. 4-5 , this measured ADC value isreferred to as an open channel voltage. The value of the open channelvoltage can be used in normalization, as described later. In addition,the value of the open channel can be used to identify cells with asingle nanopore, as described in the next section.

According to certain embodiments, as part of the open channelcalibration process, the cycle decay shape can also be determined, asdescribed above in reference to FIGS. 4-5 . For example, in response toan alternating signal (V_(LIQ)) provided to the counter electrodes, anADC may measure an output voltage on an integrating capacitor, e.g.,integrating capacitor 408 of FIG. 4 . As shown in FIG. 5 , the voltagemeasured by the ADC does not exactly track the square wave drive signal,but rather can show a decay over the bright or dark periods within eachcycle of the drive signal V_(LIQ) as a result of the buildup of chargeon C_(Double Layer). According to certain embodiments, the resultingdecay shape of each period within one AC cycle can be measured as partof the open channel calibration process. The initial value of the openchannel can help determine the expected value for the channelscorresponding to different molecules (e.g., four different bases). Forinstance, the cycle decay shape can be used to identify the spread involtages for a given threaded cycle of one tag relative to the differentin voltage expected for different tags.

In some embodiments, the open channel calibration can be performed foreach cell of the sequencing chip immediately after the poration processis complete. The open channel calibration process can leverage thepresence of open channel data during a sequencing operation, and thuscan be performed as part of a preprocessing step during the datanormalization process described in detail below.

E. Identification of Wells with Single Nanopore

As mentioned above, it is desired that each cell of the sequencing chiphave only one nanopore. According to certain embodiments, the cells withone nanopore can be identified by a statistical analysis of themagnitudes of the open channel voltages (e.g., the measured ADC valueduring bright or dark mode, without a tag present in the nanopore). Ahistogram (or distribution) of the measured voltages can be computed bybinning the measured voltages and counting the number of cells havingvoltages that fall within a particular voltage bin. The histogram can beanalyzed to determine the largest peak, i.e. the most common voltagesamongst the cells of the chip can be determined. The largest peak can beconstrained to be within a certain expected range, which may be done byexcluding a final bin of the histogram, which includes all measuredvoltages higher than a specified value.

According to certain embodiments, the most common voltages shouldcorrespond to the single nanopore cells, particularly when theelectroporation process was monitored and subject to a feedbackmechanism. Generally, the parameters of the poration process may bepreviously tuned such that for most cells, only a single pore will form,with a relatively small population forming more than one pore or no poreat all. In another embodiment, the second largest peak can be used asthe peak corresponding to cells having only one nanopore, while thelargest peak may correspond to cells with bare bilayers, i.e., zeronanopores.

FIG. 8 is a flow chart illustrating an example method 800 ofcharacterizing the number of nanopores in the cells of a sequencing chipaccording to certain embodiments. Method 800 may be performed after apore insertion process (or at least after an initial stage of poreinsertion). Method 800 may be performed by processor 224 of FIG. 2 ,digital processor 430, and/or any control logic coupled with thecircuits of the sequencing cell, including forward connections for thecontrol logic to provide control signals (e.g., to control furtherporation steps).

In step 810, open channel voltages are obtained for cells in thesequencing chip. For example, the open channel voltages can be obtainedin a similar manner to the voltages described above in reference to FIG.5 and elsewhere in this disclosure. The obtaining of the open channelvoltages may be achieved by receiving the voltages from the sequencingchip at a logic system, e.g., an FPGA, ASIC, or programmable processor.

FIGS. 9A-9C show sample open channel voltage data for different statesof the cell. FIG. 9A shows the open channel voltage data 910 (bothbright and dark periods) for a cell having a single nanopore, referredto as a single nanopore cell. FIG. 9B shows the open channel voltagedata 920 (both bright and dark periods) for a cell having zeronanopores, referred to as a zero cell bilayer. FIG. 9C shows the openchannel voltage data 930 (both bright and dark periods) for a cellhaving a short circuit, referred to as a short circuited cell.

According to certain embodiments, the open channel voltages obtained instep 810 can be single point measurements or multi-point measurements.For example, a single bright channel data point (e.g., as shown in FIG.9B) can be measured for each cell; and used to characterize the cell,i.e. whether the cell is single nanopore, zero nanopore, short, etc. Avalue for Vmzero may be subtracted from the data point for a given cell.For multipoint measurements, a collection of bright mode voltages can beaveraged together, with Vmzero subtracted before or after the averaging.A multi-point method may involve computing difference data, e.g., pointby point differences between bright and dark periods of one cycle, orpoint-by-point differences within a period of an AC cycle (e.g.,difference between first and last points within a bright period or darkperiod).

FIGS. 9A-9C show a “last point delta” (“LPD”) method that involvessubtracting the last point of a bright period from a corresponding lastpoint of a dark period, or vice versa. In FIG. 9A, the LPD 915 is about80 ADC counts and represents the LPD of a single nanopore cell. In FIG.9B, the LPD 925 is very nearly 0 ADC counts and represents the LPD of azero nanopore cell (i.e. a cell having a bare bilayer). In FIG. 9C, theLPD 935 is about 190 ADC counts and represents the LPD of a shortcircuited cell, which may correspond to a cell with no membrane ormultiple pores. While the precise values of the ADC counts for eachbilayer state may vary from cell to cell as described in further detailbelow, FIGS. 9A-9C show that in principle the LPD can be used todiscriminate between different nanopore configurations on a cell'sbilayer. Thus, according to certain embodiments, as part of step 810,the LPD of each cell is measured. As other examples, first point deltamay be used or an average point delta (a difference between the averagevalue for a bright and dark period). In other examples, the deltameasurement may be made using points within the same period, in whichcase the measurement is referred to as a “decay delta” because the deltameasures the amount of voltage decay within a cycle period, e.g., a darkdecay delta may be computed by subtracting the 5^(th) and 10^(th) pointsof a measured during a dark period. In still yet other examples, thedelta may be measured relative to a mean value that is measured outsideof a normal bright or dark period, e.g., a “zero delta” may be usedwhere the zero delta is measured between a bright period value and anoffset value, e.g., a mean offset that is measured with zero voltsapplied across the cell

In step 820, a histogram (also referred to herein as a voltagedistribution) is computed using the voltage values obtained in step 810.The histogram may take as input, any type of measured voltages includingboth single point and/or multipoint measurements. For the example of theLPD described above, to compute the histogram, the full range ofmeasured LPD values can be split into bins. For example, if the measuredADC counts range from 0 to 255, the data may be binned with one binhaving a width of 1 ADC count, thereby having a histogram with 256 bins.Other bin widths (e.g., 2, 3, etc.) are possible without departing fromthe scope of the present disclosure. Once the bin width is chosen, thenumber of cells having that particular ADC value is counted and added tothe histogram.

FIG. 10 shows sample histogram data according to certain embodiments. Arelatively large single nanopore peak 1010 is visible in the data. Theleftmost portion of the histogram shows counts for cells having zeronanopores (low voltages) and also for cell having pseudo-pores, e.g.,behavior that is similar to a pore. The rightmost portion of thehistogram shows counts for cells that have more than one pore and alsofor cells that have short circuits (e.g., no membrane).

In step 830, a histogram peak corresponding to cells having a singlenanopore is identified. According to certain embodiments, neither thepeak value, nor the peak width needs to be known in advance of obtainingthe measured voltage data in step 810. For example, a peak detectionroutine can detect boundaries and characteristics of the peaks, e.g., toidentify the single nanopore peak. For instance, the center of thelargest peak within a range can be identified as the single nanoporepeak. In some embodiments, the bins at or near the very end of thevoltage range can be ignored during the initial peak detection routine,e.g., in FIG. 10 , peak 1010 is the largest peak between bins of 2 and250. The voltage range for identifying the peak can be established viaempirical data from other sequencing chips.

In step 840, a first set of cells located within the single nanoporepeak is determined. According to certain embodiments, step 840 canidentify all cells having voltages within an identified width of thelargest peak as the set of a single nanopore cells. The width parametercan be, e.g., the full width at half maximum, which can be used as aproxy for a standard deviation. In some embodiments, the width can betaken as a specified number of standard deviations, e.g., 2, 3, 4, etc.Measurements of local minima in the histogram could also be used. Forexample, a local minimum between the zero peak and the single nanoporepeak can be used to determine a baseline for identifying the width ofthe single nanopore peak. Accordingly, embodiments can determine wherethe local maximum and minimum are within the histogram data. Theintegrated area between the various local minimum can be used toidentify the peak with the largest area, which would correspond to thesingle nanopore peak, under the assumption that this is the largestpopulation for the chip as a whole.

It should be understood that the histogram peak corresponding to cellshaving a single nanopore need not be solely comprised of single nanoporecells to the exclusion of other types of cells. As shown in FIG. 10 ,the peak is not infinitely narrow and, as such, it is understood thatwhile the population of cells within the peak will be dominated bysingle nanopore cells, some non-single nanopore cells may happen to havevoltages that fall within the peak, depending on how the width of thepeak is defined. According to certain embodiments, the width of the peakcan be defined relative to the peak value, e.g., voltage cutoffs can bechosen to be where a level of the histogram is some fraction of amaximum value of the histogram peak (e.g., full width at half max, 1/e²,99.9% level, etc.). Voltage cutoffs may be determined based on theminimum and maximum detections described above. Voltage values withinthe cutoffs define the set of cells that are to be considered singlenanopore cells, where the number of non-single nanopore cells decreasesas the width decreases. The placement of the cutoffs will involve atradeoff between capturing a large fraction of the available singlenanopore cells, while also excluding the majority of cells havingsomething other than a single nanopore (e.g., zero nanopore cells,shorts, two or more nanopore cells, pseudo-pores cells, etc).

In the example shown in FIG. 10 , cutoff 1010 is placed at 29 ADC countsand cutoff 1020 is placed at 115 ADC counts. In this example, cutoff1010 eliminates most pseudo-pores and zero-pore cells (which have openchannel voltages of less than 29 ADC counts), and likewise, cutoff 1020eliminates most multi-pore and short circuited cells (which have openchannel voltages of greater than 115 ADC counts). According to certainembodiments, to improve the accuracy of the chip, cells having voltagesoutside of the cutoffs may be deactivated or their outputs may beselectively removed or ignored.

In general, when determining the cutoffs using the histogram data,certain artifacts may be present that depend on the chosen location andwidth of the histogram bins. In some implementations of step 840, theset of single nanopore cells can be determined using a kernel densityestimate (KDE) or other smoothing function to avoid and/or minimizehistogram artifacts. In general, a KDE is used to estimate theunderlying distribution function of a set of noisy data points. Morespecifically, a KDE process can build an estimated distribution functionof the measured data from an admixture (or sum) of characteristicfunctions, one per measured data point. For example, a KDE may take asinput the measured voltage data points, and for each voltage data point,compute the values of a continuous characteristic function (e.g. aGaussian) with a location centered at the voltage value of that datapoint. This can be done for each data point, and the results can besummed to give the KDE of the underlying distribution function. Anotherway to visualize the KDE process is that to compute the KDE, thecharacteristic function (e.g., a Gaussian) is moved as a window functionover the histogram, with the width of the window defined by the Gaussianwidth parameter. The choice of how to define the centers of eachcharacteristic function is analogous to choosing the bin size for thehistogram computation and will in general depend on the details of thedata itself. In one implementation, the window is moved 0.2 of ADCvalue. In other embodiments, rather than using a uniform step for movingthe characteristic function, the characteristic function can be computedwith the center located at every measured voltage value in the measureddata.

FIG. 11 shows a KDE of voltage values for different cells according tocertain embodiments. The first peak 1101, which is small, corresponds tothe cells with no nanopores. The second peak 1103 corresponds to thecells with one nanopore. The third peak 1105 and the fourth peak 1107correspond to cells with two nanopores and three nanopores,respectively.

In step 850, a sequencing operation may be performed using only theidentified single nanopore cells. The sequencing operation may proceedas described above in reference to FIG. 3 .

Determining the set of single nanopore cells from a histogram (ordistribution) of the measured open channel voltage data can be a robustprocess for identifying cells with a single nanopore because minimalassumptions may be made, as opposed to using fixed cutoff values. As thecells may vary from chip to chip and different biochemistry may beinvolved, such a robust process is desirable. For example, one may notknow the exact value of the voltage for each single nanopore peak, andvarious nanopores may be used for different chips. Furthermore, thelipid bilayer can change over time. As the gain of a cell depends onboth R_(pore) and C_(bilayer), a larger well or different solvent (ordifferent annulus) can change gain, and therefore the open channel andthreaded voltages.

V. Normalization

Once the usable cells of a chip are identified, a production mode can berun to sequence nucleic acids, one for each usable cell. The ADC valuesmeasured during sequencing can be normalized to provide greateraccuracy. In some embodiments, the voltage level data that is acquiredduring a bright period of the AC drive voltage (referred to herein asthe “bright mode voltages” or alternatively as the “bright periodvoltage”) are normalized. For example, the bright mode voltages can benormalized by dividing each measured bright mode data point by thebright mode voltage of the cell when the nanopore is in an unthreadedstate, referred to herein as the “open channel voltage” or “bright modeopen-channel voltage.” By normalizing the bright mode voltage leveldata, the dynamic range of the raw ADC measurements is rescaled to anormalized range, generally to provide a range between 0 and 1, althoughvalues greater than 1 are possible, depending on the specific value usedfor bright mode open-channel voltage.

Normalization can allow compensating for changes to the system, e.g.,changes in the electrical properties of a sequencing cell. For instance,the capacitances of circuit 400 may change over time. For example, thecapacitance of capacitor 426 (C_(Bilayer)) because of physical changesin the bilayer area or thickness, e.g., at the edges of a well, wheresuch change is referred to as gain drift. As another example, charge canbuild up in the cell as a result of differences in charge transferbetween bright periods and dark periods, which is referred to asbaseline shift (and sometimes fast baseline shift). A slow baselineshift can be caused by variability in the measurements circuit andchanges in the electrical properties of the bilayer membrane. Theseexamples are described in more detail below.

Such changes can affect the values measured for the exact same state,thereby causing instabilities. However, normalization can compensate forsuch changes to provide normalized values (e.g., voltages or currents)that are stable over time, thereby allowing greater accuracy indetermining the sequence of a nucleic acid.

A. Idealized Normalization

FIGS. 12A-12B illustrate the concept of normalization for an idealizedADC signal according to some embodiments. FIG. 12A shows idealizedbright mode data 1201 and idealized dark mode data 1203 as might bemeasured by an ADC during a sequencing operation, e.g., as describedabove. The idealized ADC data of FIGS. 12A-12B is also shown on a muchlonger timescale than, e.g., the data described above in reference toFIG. 5 . As such, the individual AC cycles are not visible, as is thecase in FIG. 5 . Nevertheless, it should be understood that bright modedata 1201 and dark mode data 1203 are acquired during different halfcycles of the AC drive voltage V_(liq). Furthermore, the data shown inFIG. 12A are idealized in the sense that no noise, gain drift, and/orbaseline shift is present, i.e., the open channel voltages (both brightmode and dark mode) are constant over an individual AC cycle as well asconstant over time. In addition, bright mode data 1201 shows threadingevents 1205 and 1207 that correspond to two separate hypotheticalthreading events of two different tagged nucleotides. The measuredvoltages at threading events 1205 and 1207 are different due to thedifferent tagged nucleotides being threaded. As shown here, thethreading events last over several AC cycles and occur on a fast enoughtimescale that during the threading events, no bright mode open-channelsignal is measured.

In FIG. 12A, the open channel ADC value for the bright mode isrepresented by P₀, which may be used to normalize the ADC values forthreading events 1205 and 1207. This normalization factor P₀ in thisidealized example is constant at the measured value at t=0, which is 150ADC values in this example. To perform the normalization in this case,all of the bright mode data can be divided by the same constant: P₀=150.For ease of description, the example of normalization by division isused throughout disclosure; however, one of ordinary skill willunderstand that multiplication by the inverse is mathematicallyequivalent and thus may also be used without departing from the scope ofthe present disclosure.

FIG. 12B shows the normalized bright mode data 1210 resulting fromnormalizing the idealized bright mode data 1201 of FIG. 12A. In thenormalized bright mode data, the open-channel level and the tag levelsare not the raw ADC values, but rather span the range from 0 to 1.Because the bright mode open channel voltage is constant in this case,the normalization factor P₀ can be used to normalize the entire signalacross the entire duration of the sequencing run. However, real signalssuffer from a number of non-idealities that make this simple,single-valued normalization inaccurate. Two primary causes of errors inreal sequencing systems are baseline shift and gain drift.

B. Gain Drift

Each sequencing cell has a voltage gain that depends on the lipidbi-layer capacitance. The gain corresponds to the voltage differencethat is achieved between the two electrodes (e.g., counter electrode 210and working electrode 202). For example, given the equation of C=q/V fora capacitor, as the capacitance increases, the voltage would decreasewhen a same amount of charge is present. Accordingly, if the lipidbi-layer capacitance changes over time, then the voltage gain changesover time. If the voltage gain changes over time, then the bright modeand dark mode (both open channel and threaded) can change over time. Inany real system, the bilayer capacitance may change over time, e.g., asthe bilayer deforms. Such changes typically occur on the timescale ofhundreds or thousands of seconds and, though slower than a typicalthreading event, still should be accounted for if high accuracymeasurements are desired.

FIG. 13 shows an idealized signal that suffers from gain drift (withnon-realistic timescales for both threading events and non-realisticgain drift to allow for both phenomena to be clearly shown on the samegraph). Like FIGS. 12A-12B, FIG. 13 shows an idealized bright mode data1301 and idealized dark mode data 1303 as would be measured by an ADCduring a sequencing operation. The gain drift is illustrated as theoverall drift in the open channel voltages for both bright and darkmodes, with the drift being anti-correlated between bright and darkmodes (e.g., when the bright mode increases, the dark mode decreases andvice versa). For the sake of clarifying the effect that gain drift hason measured ADC level of the same tag over time, 4 threading events arealso shown, with each threading event involving the same species of tag,resulting in a same drop in voltage from the current open channelvoltage. However, despite the fact that the same tag is being threadedduring each event, the ADC value of this tag drifts over time. Thus, itcould be the case that for this cell, the same tag could be detectedanywhere within a range of 120 to 150. As a result, non-normalizedlevels would be error prone.

To correct for the gain drift, a normalization procedure similar to thatdescribed above in reference to FIGS. 12A-12B may be performed. However,unlike the case in FIGS. 12A-12B, the open channel voltage in the brightmode is not constant over time, so the single value normalizationdescribed above (i.e., divide everything by P₀) fails to normalize theentire signal over time. Instead of the constant normalization, a morecomplex variable normalization can be applied, e.g., the normalizationcan be accomplished by dividing each raw bright-mode measured ADC valuewith an estimate of that point's open channel value. For eachnon-threaded region, an estimate of the open channel voltage can be madeby any number of ways, e.g., by taking a local mean value or by usingmore sophisticated signal processing technique such as a Kalman filter,as described in more detail below. Thus, a local estimate can beobtained for the open channel value for the bright mode, so as tonormalize a data point using the estimated voltage that is local to thatdata point.

On the other hand, the threaded regions of the signal can provide achallenge. For some threading events, there may open channel dataavailable if the threading rate is slow enough, e.g., as shown inthreading events 1305 and 1307. When the threading rate is relativelyslow, open channel values can be measured before the tag is threaded.Such open channel values can be measured for each cycle. This behavioris depicted in the comb-like lines shown for threading event 1305 and1307. In these cases, the limited open channel data may be used toestimate the true open channel value during the threading event. Thislimited open channel data (i.e., limited relative to when no threadingoccurs) can be used to obtain a local estimate of the open channel value(e.g., local within time, so as to account for gain drift)

However, it may be the case that the threading is fast enough that noopen channel data is captured in the bright mode, e.g., as shown forthreading events 1309 and 1311. When the threading rate is sufficientlyfast, the tag is immediately threaded, and no open channel values aremeasured. This lack of open channel voltages can be problematic whentrying to determine a local estimate of the open channel; if there arenot open channel values for a given time interval, no local estimate canbe determined for that time interval. In these cases, it is possible todetermine the local estimate for the open channel data in the brightmode using the dark mode data, as described in further detail below.

C. Baseline Shift

Baseline shift is a phenomenon that is related to charge imbalances thatbuild up on certain elements (e.g., C_(Double Layer)) in the cell duringthe charging and discharging cycles that take place during themeasurement process. For example, during the measurement process, excesscharge can build up on the working electrode of the cell, represented byC_(Double Layer) in FIG. 4 . In one example, the charge imbalance iscaused by the fact that both the nanopore and the tags have non-linearI-V characteristics. As a result of this nonlinearity, a charge anddischarge cycle may not add or remove the same amount of charge to thecapacitive elements. For example, negative and positive ions may notmove from one electrode to the other electrode via the pore at the samerate over time, e.g., causing positive charge to build-up in the well.Note that the duty cycle can be 60% dark mode and 40% bright mode toaddress a typical difference in transmission rate of positive andnegative ions, but when a rate changes, the duty cycle would have tochange, which can be difficult to do.

As a result of this accumulated charge imbalance, the voltagemeasurements in a cell would increase (e.g., when positive charge buildsup in the well). This shift in a baseline voltage can increase until itproduces a voltage high enough to counterbalance the opposing voltageoriginally set up as a consequence of the charge imbalance. At whichpoint, the charge can re-balance. Baseline shifts can occur in the boththe dark mode and bright mode open channel states and in each of thefour threaded states, with the magnitude and time constants for theshifts potentially being different in each of the open channel and fourthreaded states. As a result, the baseline shift can change in agenerally random way that mirrors the stochastic binding events of thetags at the pore.

FIG. 14 shows an idealized signal that exhibits baseline shift. LikeFIGS. 12A-12B and FIG. 13 , FIG. 14 shows an idealized bright mode data1401 and idealized dark mode data 1403. This type of baseline shiftgenerally occurs on a timescale that is on the order of the dwell timefor a tag in a pore, a timescale that is generally much faster than thetimescale for gain shift. Thus, gain shift is shown in FIG. 14 .

Before a threading event 1410, the cell has reached equilibrium, i.e.,the baseline voltage is what it needs to be to ensure equal chargetransfer, e.g., to C_(Double Layer) during the bright and dark modes.However, once the threading event 1410 begins, the system is driven outof equilibrium. More specifically, while effective resistance of thepore when the cell is in the dark mode stays the same, the effectiveresistance of the pore in the bright mode has increased. The increasedresistance in the bright mode causes less charge to move during thismode, as compared to before the threading event occurred. Thus, a chargeimbalance begins to form, which leads to an upward shifts 1405 and 1407in both the tag level and the dark mode open channel level,respectively.

As with the gain shift phenomenon, to compensate for baseline shift, avariable, point-by-point normalization can be applied, e.g., thenormalization can be accomplished by dividing each raw bright-modemeasured ADC value with an estimate of that point's open channel valueas described in further detail below. Such an estimate can be considereda local estimate as it is valid for a single point or a certain set ofpoints within a time interval.

D. Other Offset Effects

There are various offset effects that can be accounted for innormalization without departing from the scope of the presentdisclosure. Some of these offset effects are described below

1. Intracycle Decay Cycle Shape

Ideally, the open channel voltage would be constant over a cycle inwhich no threading exists, and likewise the threaded voltages would bethe same for a given bright mode cycle. Such constant behavior wouldprovide a larger difference in the values for open channel versus thethreaded voltages for different bases. Further, having constant behaviorwould allow easier discrimination between voltage levels. For instance,the peaks would be sharp in a histogram of the number of measuredvoltage values across a sequencing of a nucleic acid, as there would beless spread in values for open channel or any given threaded channel.

But, the measured voltage values during a cycle vary due to intracycledecay, as mentioned above. This intracycle decay is a result of theC_(Double Layer) (capacitor 424 in FIG. 2 ) changing from onemeasurement to another during a cycle. This change in C_(Double Layer)affects the decay rate of the voltage at the integrating capacitor sothat the decay is slower for successive measurements, thereby resultingin slight changes in the measured ADC value.

To compensate for such changes, one could take just a single voltagereading, but that may not be as accurate as a multiple measurements.Some implementations can effectively get a single measurement by takingan average (mean) of the voltages over a given cycle. Such an averagecan be weighted based on a calculated or expected value for theintracycle decay rate. Such an average can be used as a measured ADCvalue, potentially where threaded voltage in a cycle can be given thevalue of the average.

2. Charge Injection Offset

An offset can also occur as a result of charge that is injected tocircuit 400 via switch 401 of FIG. 4 . Switch 401 is used for resettingthe voltage on integrating capacitor 408 in order to take a newmeasurement using the ADC. Each time the switch closes, an amount ofcharge is injected into the circuit for that cell. For the chargeinjection, there is a transfer of certain number of electrons from asource to the drain, thereby dumping a certain amount of charge into thesystem. The charge distributes among the capacitors, which creates anoffset. The offset would be acceptable if it was constant, but it is notconstant.

Examples for why such a charge injection offset can vary are as follows.Over time, the surface area of the bilayer can become larger or smallerbilayer (e.g., by the annulus at the edges creeping in and out). Thischange can cause a ratio of the capacitance of the bilayer to changerelative to the capacitance the integrating capacitance (e.g., 408).This ratio affects the time constant of the circuit, and thus what themeasured voltage after a specific amount of time, as can be measured bythe ADC. If the ratio is determined only once, this ratio value can bebecome outdated, and thus incorrect. Embodiments can use the magnitudeof the charge injection, the capacitance of the bilayer, and how it ischanging to determine a normalization to compensate for the chargeinjection offset.

Using FIG. 4 as an example, the switch 401 resets the voltage of thesystem, after which an ADC value is measured at a specified amount oftime after the switch 401 is opened. The resetting and the measuring isrepeated. As the switch is non-ideal, every time the switch 401 close,some charge is injected into the circuit. Charge builds up on C_(bl)426, thereby causing the baseline voltage to change as charge builds upon C_(bl).

When the charge is injected, the charge is distributed in the circuit.The primary places are C_(bl) 426 and integrating capacitor 408. Theratio of the charge between the two capacitors depends on the size ofthe bilayer. The offset of a particular cell changes over time, as thevoltage changes on integrating capacitor 408. If the ratio stayed thesame, then it would not change the measured offset, as it would stay thesame over time. But, as C_(bl) 426 changes, different amounts of chargedwill be injected to C_(bl) 426 and integrating capacitor 408, therebychanging the offset. Such a problem would not exist if the capacitancesdid not change over time, as is typical for semiconductor capacitors,but is not true for biochemical elements that act as capacitors.

As a solution, C_(bl) 426 can be measured over time. The capacitance ofintegrating capacitor 408 would not typically change over time, as itcan be a semiconductor element. The charge can be quantified at thebeginning of a sequencing run, and may be different for each cell. Thischarge can be determined as part of calibration, e.g., as part ofdetermining VMzero. C_(bl) 426 can be measured using the first pointdelta, which is the difference in the first voltages measured for brightand dark modes after a cycle switch in polarity, e.g., of a square wave.There is a relationship between the first point delta (FPD) and C_(bl)426. Such a relationship can be constant from cell to cell.

Accordingly, the change in FPD can be used to determine the change inthe offset of VMzero. The relationship is based on the amount of chargeinjected into the system as measured for a beginning cycle, the value ofintegrating capacitor 408 for the cell, the initial measurement ofC_(bl) 426, and the change in FPD of the beginning cycle.

The following technique can be used to determine a change to VMzero as aresult of the charge injection. The charge q=C*V, where q is charge, Cis capacitance and V is voltage. C=C_(bl) 426+C_(ncap) (integratingcapacitor 408). V=q/(C_(bl)+C_(ncap)), and the change in voltage due toa changing bilayer cap is: dV=q(1/(C_(bl_new)+C_(ncap))−1/(C_(bl_old)+C_(ncap))). This change involtage can be used to modify an ADC value before other normalization,e.g., to compensate for gain drift or baseline shift.

E. Hybrid-Online Normalization Method

While FIGS. 12-14 discussed gain drift and baseline shift as separateeffects, these phenomena are likely to both occur together, likely withadditional noise sources. For example, FIG. 15 shows test data from asequencing cell at two different timescales showing both gain drift andbaseline shift, as well as other more uniformly distributed butrelatively fast random noise, e.g., noise that may originate from theADC. In order to normalize the real signal shown in FIG. 11 , theconcepts from the description above can be unified into a singlenormalization method, referred to herein as the “hybrid-online”normalization method.

In an embodiment of such a hybrid-online technique, each bright modevalue can be normalized by dividing that value by an estimate of thecurrent (local) bright mode open channel value. Where possible, theestimate of the open channel value is determined from the bright modedata itself. However, in some cases, there is no bright mode openchannel data; in that case, embodiments can infer the open channelvalue, e.g., compute the bright mode open channel level from the darkmode data in combination with an analytical model of the cell. Thisinferred bright mode open channel value can then be treated just likethe values that are directly measured, and fed into a filtering process,e.g., a Kalman filter, to obtain the best estimate for the actual openchannel value. The best local estimate can then be used to normalize thedata.

FIG. 16 shows a flow chart for a hybrid-online normalization methodaccording to one or more embodiments. FIG. 16 is described in relationto FIG. 17 and can be performed by a computer system, which may beconnected with a sequencing device. FIG. 17 shows somewhat realisticsample data to aid in the description of the method. FIG. 17 is similarto the previous figures in that it includes idealized bright mode data1701 and idealized dark mode data 1703, but also includes random noise.Data points represented by “x's” show the raw ADC values measured overtime. Data points represented by circles show the filtered ADC data,e.g., circles show the output from a Kalman filter (or the like). Datapoints represented by triangles represent computed open channel datathat is computed by the Kalman filter (or the like), but using thenegative open channel signal, which may use an analytical system model,as will be described in further detail below. For reference, thestraight lines represent the (unmeasurable) “ground truth” open channelvalues for both the bright and dark modes.

In step 1601 a raw bright mode data point is measured, e.g., raw brightmode value 1605 shown in FIG. 17 . According to some embodiments, thisdata point is measured by an ADC and sent to a digital processor, e.g.,ADC 410 and digital processor 430, respectively, as shown in FIG. 4 .The digital processor can be part of a computer system that includesother components. In this example, raw bright mode value 1705 occursduring a threading event and is therefore systematically lower than theopen channel data points.

In step 1603, the digital processor tests to see if there are any brightmode raw open channel (OC) values that correspond to the measured brightmode value 1705. Whether a bright mode OC value corresponds can bedetermined based on a timing threshold, e.g., whether the two values arewithin a specific time interval. The criterion of a time interval canensure that the estimate is local in time, and therefore an accurateestimate. The length of the time interval can vary based on the timescale(s) of the offset(s) that are being compensated. In someembodiments, a sufficient number of OC values satisfying the timingthreshold can be required, e.g., using a count threshold.

If there is one or more bright mode raw OC values that do correspond toa current raw bright mode value (e.g., 1705), the bright mode raw OCvalues can be filtered. In this example, the threading rate associatedwith this particular threading event is slow enough that indeed there isa bright mode raw OC value, e.g., data point 1707. Note that in thisexample, due to the phenomena of baseline shift, both the OC values andthe threaded values are trending upward as the cell attempts to settleto a new equilibrium value. Some embodiments may restrict the range ofADC values for which OC data can be found to only data points within arange between and upper threshold 1709 and a lower threshold 1711. Theparticular values for upper threshold 1709 and lower threshold 1711 canbe chosen so that the range of values straddles the expected range ofvalues for the bright mode OC values so that the filtering process doesnot inadvertently select a threaded value as an OC value.

Because there does exist a bright mode OC value, the method proceeds tostep 1607 for computing a filtered OC value from the raw OC value. Onepurpose of applying a filter to the raw data in step 1607 is to moreaccurately estimate a true open channel value from the selected raw openchannel value. The filter may be a discrete recursive filter such as aKalman filter, as described in more detail below. The filter can take asinput a bright mode raw OC value (e.g., data point 1707) and output afiltered OC value 1713 that is closer to the ground truth OC value. InFIG. 17 , the set of filtered OC values are represented by circles. And,as can be seen form the comparing the raw data to the filtered data, thevariance of the filtered data is lower than the raw data. Thus, thefilter can act as a type of low pass filter in this instance.

In step 1609, the measured bright mode value 1705 is then normalizedusing the filtered OC value 1713, e.g., by dividing measured bright modevalue 1705 by filtered OC value 1713.

In step 1611, the normalized bright mode value (not shown) is thenoutput and or stored in memory to be later used during a base callingprocess.

Returning to the first step in the method shown in FIG. 16 , analternative case where no bright mode OC data is available will now bedescribed.

In step 1601, a raw bright mode value is measured, e.g., raw bright modevalue 1715 shown in FIG. 17 . In this example, raw bright mode value1715 occurs during a threading event and is thus, systematically lowerthan the open channel data points.

In step 1603, a computer system can test to see if there are any brightmode raw OC value(s) that corresponds to the measured bright mode value1715; if present, the raw OC value(s) are selected to be filtered. Inthis example, the threading rate associate with this particularthreading event is fast enough that there is no corresponding brightmode raw OC value. Accordingly, the method proceeds to step 1605.

In step 1605, the computer system computes an estimate of the brightmode raw OC value using a dark channel value, e.g., dark channel value1717. In some embodiments, the dark channel value can be used as thebright mode raw OC value. In various embodiments, the dark channel valuemay be a filtered dark channel value (e.g., that has already beencomputed using a filter running separately on values of the dark channeldata) or may be a raw dark channel value.

In some implementations, the computation of the estimate can use ananalytical model of the cell circuit, e.g., as shown in FIG. 4 , tocompute the estimate of the bright mode raw OC value. Such an analyticalmodel can be used when two offsets act in a different manner, e.g.,multiplicative in magnitude (e.g., gain drift) and a common shift(baseline shift). For example, let P_(t) correspond to the open channelbright value at a given time t, and let P₀ represent an initial value ofthe bright channel. Likewise, let N_(t) represent the dark channel valueat time t and let No represent an initial value of the dark channel. Themodel then includes the set of linear equations:P(t)=m*(1+b*c)*P ₀  (1)N(t)=m*(1−b)*N ₀,  (2)where m is the gain drift, b is the baseline shift, and c is thebaseline shift ratio between the bright channel and the dark channel. Inprinciple, the system of equations (1)-(2) is an overdetermined one.There are 2*N number of equations with N being the number of data pointsacquired in the bright and dark channels; stated another way, Equations(1) and (2) are defined for each measured data point. Accordingly, allunknown constants can be solved if there are a sufficient number of datapoints measured. In practice, the value of c can be determinedempirically, e.g., in an offline manner using the whole dataset, whilethe values of b and m can be computed online (e.g., as the data is beingparsed from beginning to end), one b and m being computed for each ofthe measured data points.

With knowledge of c and either b or m, Equations (1) and (2) can becombined to provide a closed form solution for the current bright modeopen channel value P_(t) as a function of the dark channel data N_(t)and No and the initial bright channel value P₀. Accordingly, in step1005 the P_(t) serves as the estimate for the raw bright mode OC value,even when no bright mode data exists because the threading rate is toofast. As gain drift m changes slower than baseline shift b, values forP(t) and N(t) can be used to compute m and b. Then, at a later time,when a P(t) is not available, the previously computed gain drift m canbe used. In some embodiments, gain drift m and baseline shift b can befiltered (e.g., moving averages of them) and then used to determineP(t). Thus, step 1605 can involve a corresponding bright mode OC valueand a corresponding dark mode OC value.

In step 1607, the raw OC value computed from Equations (1)-(2) is thenfiltered like a directly measured OC value, i.e., it is passed to afilter to then compute a filtered OC value that is an improved estimatefor the actual OC value absent measurement noise. FIG. 17 shows oneexample of a filtered OC value 1719 (shown as an open triangle) that hasbeen computed using dark channel value 1717 in combination with themodel of Equations (1)-(2).

Steps 1609 and 1611 proceed in a manner identical to the case whenactual bright mode OC data is available and directly measured. Morespecifically, for this example, in step 1609 the measured bright modevalue 1715 is normalized to the filtered OC value 1719, e.g., bydividing measured bright mode value 1715 by filtered OC value 1719.

In step 1611, the normalized bright mode value (not shown) is thenoutput and or stored in memory to be later used during a base callingprocess.

In the example method shown in FIG. 16 , and as described in furtherdetail below in reference to Eqns. (7)-(8), some embodiments may employa single channel filter (single-channel in the sense that the filtermodel is a one dimensional model of P(t)). In these embodiments, theoutput of the filter is a single value representing the best estimate ofthe bright mode open channel value, P(t). All other values are eitherobtained directly from the measured data (bright and dark mode) and/orcomputed from the model represented by Eqns. (1) and (2) using themeasured data as input to the model.

Other methods of employing filtering may be employed without departingfrom the scope of the present disclosure. For example, in someembodiments, a multi-channel filter may be employed (e.g., an “extendedKalman filter”) and one or more of P(t), N(t), m(t), and b(t) may betracked and filtered by separate filter channels as described in furtherdetail below in reference to Eqn. (9). In some embodiments, the filtermay include additional channels to track all four bright mode threadedvalues in addition to P(t), N(t), m(t), and/or b(t).

F. Kalman Filter for Computing Filtered Open Channel Values

A Kalman filter is a Bayesian estimator. There is some prior probabilitythat corresponds to the believed system state, some observation, andsome weighting between the two based on the confidence in the two. It isan iterative process in that for every observation there is an update ofthe estimate of the state, and update the estimate of the uncertainty inthat state. More specifically the Kalman algorithm comprises two mainsteps: a “predict” step and a “correct” step. In the “predict” step, thenew state of the system and new error covariance of the data ispredicted based on the prior values according to the followingequations:{circumflex over (x)} _(k) ⁻ =A{circumflex over (x)} _(k-1) +Bu_(k-1)  (3)P _(k) ⁻ =AP _(k-1) A ^(T) +Q,  (4)where equation 2 describes how to predict a new a priori state estimate{circumflex over (x)}_(k) ⁻ from the state at a previous time step{circumflex over (x)}_(k-1) and where Equation 3 describes how topredict a new a priori estimate for the error covariance from the errorcovariance at the previous time step. Once the new a priori estimatesfor the process state and covariance are obtained, these values are“corrected” based on information obtained by the actual measurement ofthe process state. The “correction” step proceeds according to thefollowing equations:K _(k) =P _(k) ⁻ H ^(T)(HP _(k) ⁻ H ^(T) +R)⁻¹  (5){circumflex over (x)} _(k) ={circumflex over (x)} _(k) ⁻ +K _(k)(z _(k)−H{circumflex over (x)} _(k) ⁻)  (6)P _(k)=(1−K _(k) H)P _(k) ⁻  (7)

In the “correction” step, the first thing that is done is to compute theso-called Kalman gain K. Next, the process is measured to obtain theactual state measurement z_(k). Next, the a posteriori state estimate{circumflex over (x)}_(k) is computed from the previously computed apriori state estimate {circumflex over (x)}_(k) ⁻ by incorporating themeasurement z_(k) as shown in Equation (6). Finally, the a posteriorierror covariance estimate P_(k) is computed from the previously computeda priori state estimate error covariance estimate P_(k) ⁻ according toEquation (7). These new estimates are then fed back into another“predict” step at the next time step and the procedure continuesrecursively as more data comes in.

Because of its discrete, recursive nature, the Kalman filter does notrequire large amounts of data, but can just look at one piece of data ata time. It is good for a stream of data, and can work with GPUs, as GPUscannot hold a large section of data trace in memory. This is unlike lowpass filter or Fourier transform where all the data is needed in memory.

In various embodiments, either a Kalman filter or an nonlinear extensionof the Kalman filter known as the Extended Kalman filter may be employedto provide estimates for either the dark mode values or the bright modeopen channel values. For the case of the linear Kalman filter, the modelfor the bright mode and/or dark mode open channel values is a constant,one dimensional process, i.e.,{circumflex over (x)} _(t) =P _(OC)(t)=C  (8)P _(t) ≥P _(k)(t)  (9)where C is a constant.

For the extended Kalman filter, the process is modeled according to thefollowing three dimensional vector:

$\begin{matrix}{{\hat{x}}_{t} = \begin{bmatrix}{P_{OC}(t)} \\{N_{OC}(t)} \\{b(t)}\end{bmatrix}} & (10)\end{matrix}$where P_(OC)(t)=P_(OC) ^(k+)(1+cb), N_(OC)(t)=N_(OC) ^(k−)(1−b), b(t)=B,and where B a constant, b is the baseline shift, and c is the baselineshift ratio between the positive cycle and the negative cycle. Accordingto this embodiment, f is an empirically determined constant and P_(OC)^(k+) and N_(OC) ^(k−) are the Kalman predictions of the open channellevels in the bright and dark modes, respectively. Embodiments thatemploy an extended Kalman filter may be beneficial if the variousprocesses being modeled (e.g., the bright mode open channel voltageP_(OC), the dark mode open channel voltage N_(OC), and baseline drift b)are independent and/or vary according to different timescales.

G. Method Using Dark Channel to Normalize Threaded Voltages

As exampled above, an open channel voltage may not always be available.In such circumstances, an open channel dark voltage may be used.

FIG. 18 shows a flow chart for a normalization method using open channeldark period voltages according to one or more embodiments. FIG. 18 canbe performed by a computer system, which may be connected with asequencing device, e.g., as described above in reference to FIGS. 1-4 .

In step 1810, a plurality of measured voltages are obtained for asequencing cell. For example, during the measurement, a voltage can beapplied across the sequencing cell, which includes a nucleic acid. Theapplied voltage may be an alternating signal, e.g., an AC signal havinga first portion (e.g., a bright period, also referred to herein as a“bright phase”) and a second portion (e.g., a dark period, also referredto herein as a “dark phase”) relative to a reference voltage. Accordingto certain embodiments, the reference voltage may be a reference voltage(e.g., V_(PRE) 405 in FIG. 4 ) that is applied to an integratingcapacitor, e.g., n_(cap), as shown in FIG. 4 . According to someembodiments, voltages can be measured by an ADC and sent to a digitalprocessor, e.g., ADC 410 and digital processor 430, respectively, asshown in FIG. 4 . The digital processor can be part of a computer systemthat includes other components. The voltages may also be obtained byreceiving the voltages at a processor of a computer system.

In step 1820, a first set of one or more voltages measured in step 1810during the first portion of the alternating signal is determined, e.g.,one or more voltages measured during the bright period of thealternating signal are selected by the digital processor 430. The firstset of voltages may correspond to various bright periods. Suchmeasurements can occur as described herein and can occur at varioustimes of sequencing different parts of a nucleic acid of a given cell.

In step 1830, a second set of one or more voltages measured during thesecond portion of the alternating signal is determined. For example, thesecond set of one or more voltages can be measured during the darkperiod of the alternating signal in step 1810 and can be selected by thedigital processor 430. The second set of one or more voltages can bemeasured across various dark periods. According to certain embodiments,the first set of one or more voltages and second set of one or morevoltages are determined with no molecule in a nanopore of the sequencingcell, i.e., when the cell is in an open channel state. These voltagesare referred to herein as open channel voltages, as described in moredetail above in reference to FIGS. 3-5 .

In step 1840, a normalization factor is determined based on the secondset of one or more voltages, i.e., the normalization factor isdetermined based on the measured one or more dark period voltages.According to certain embodiments, the normalization factor may be abright period open channel value computed using Eqns (1) and (2) basedon the one or more dark period voltages, e.g., as described above inreference to FIG. 16 , and in particular in accordance with the modeldescribed in step 1605. In some embodiments, the normalization factorcan be the one or more dark period voltages themselves. Like the methoddescribed above in reference to FIG. 16 , the normalization factor (andindeed any of the measured or computed values) may be filtered, e.g.,using a discrete recursive filter, or the like.

In step 1850, a third set of one or more voltages measured during thefirst portion of the alternating signal is determined. According tocertain embodiments, the third set of one or more voltages may bemeasured when a tag molecule is threaded in the nanopore of thesequencing cell, the tag molecule corresponding to a particularnucleotide. Examples of the third set of voltages are often referred toherein as bright mode threaded voltages. The third set of one or morevoltages may be measured across various bright periods.

In step 1860, the third set of one or more voltages is normalized usingthe normalization factor. As described above in reference to FIG. 16 ,the normalization may be a computed (i.e., estimated) open channelvoltage and the normalization is accomplished by dividing by thecomputed open channel voltage. Alternatively, the normalization may beaccomplished by a division by the reciprocal of the computed openchannel voltage.

The normalized voltages can be used to determine a sequence of thenucleic acid. The normalization allows voltage levels corresponding todifferent tags to be consistent over time, and thus allowing bases to beaccurately determined.

H. Method Compensating for Gain Drift and Baseline Shift

FIG. 19 shows a flow chart for a normalization method compensating forgain drift and baseline shift according to one or more embodiments.

In step 1910, a set of measured voltages is obtained for a sequencingcell in a manner similar to that described above in reference to FIGS.16 and 18 .

In step 1920, an initial voltage measured during the first portion ofthe alternating signal is determined. According to certain embodiments,the first portion of the alternating signal can be the bright period (orbright phase) of the alternating signal as described above in referenceto FIGS. 16 and 18 . The initial voltage is initial as it comes beforelater measured voltages, and does not imply that it is a very firstvoltage to be measured in time.

In step 1930, subsequent voltages measured at various times aredetermined. For example, subsequent voltages can be measured during oneor more bright and or dark periods of the alternating signal, during oneor more cycles of the AC signal.

In step 1940, the subsequent voltages are used to solve a set ofequations for a gain drift m and a baseline shift b. For example, Eqns.(1) and (2) described above may be used. According to certainembodiments, the gain drift m can result from a capacitance of a circuitof the sequencing cell changing over time, and the baseline shift b canresult from an accumulation of charge of the circuit of the sequencingcell over time. As shown by Eqns. (1) and (2) above, each value of thesubsequent voltages can be defined as including the gain drift m and thebaseline shift b.

In step 1950, the gain drift m, the baseline shift b, and the initialvoltage are used to determine a normalization factor. According tocertain embodiments, the normalization factor may be a computed, i.e.,estimated, bright mode open channel voltage that results from a modellike that described in Eqns. (1)-(2).

In step 1960, one or more voltages are determined, where these voltagesare measured during the first portion of the alternating signal when atag molecule is threaded in the nanopore of the sequencing cell, the tagmolecule corresponding to a particular nucleotide. These one or morevoltages determined at this step are also referred to herein as brightchannel threaded voltages.

In step 1970, the one or more voltages are normalized using thenormalization factor. The normalization is accomplished in the samemanner described above in reference to FIGS. 16 and 18 . The normalizedvoltages can then be used to determine a sequence of a nucleic acidbeing sequenced in a given cell.

Like the method described above in reference to FIG. 16 , the methodsdescribed in FIGS. 18-19 can normalize a sequencing signal withoutrequiring any measured bright mode open channel data and thus can resultin an improved sequencing system that can produce more robust andaccurate sequences. To accomplish the normalization, embodiments canestimate the open channel voltage to be used for normalization from thedata that is available, potentially just dark mode open channelvoltages. According to certain embodiments, the estimate may be made bya digital processor that takes measured dark mode voltages as input intoa model that predicts what the corresponding open channel voltage wouldbe based on the model of the cell, e.g., Eqns (1)-(2) described above.Accordingly, embodiments can allow for improved normalization of thesignal in cases where there is fast threading and no bright mode openchannel data available.

While the methods described above, e.g., in reference to FIGS. 6-8, 16,18, and 19 , relate to calibration and/or normalization of signal valuesthat represent voltages, other types of signals are possible and thusother types of signal values may be processed without departing from thescope of the present disclosure. For example, the circuitry of a cellmay be configured such that signal values represent measurements ofvoltages, currents, or any other quantity (e.g., time) that may be usedto derive the voltage and/or current at any point in a circuit of thesequencing cell.

I. Summary

As discussed above, a new P(t) and N(t) may be determined for eachcycle. For each switch to modulate the voltage to be positive (breakperiod), a new P(t) can be determined, e.g., at 80 Hz. In someembodiments, using data points from the bright and the dark modes asindependent observations helps to provide greater accuracy in thenormalization. Additionally, during threading events, the dark mode canact as a built-in calibration source because the threading events haveunknown voltage levels, and thus are difficult to use for normalization.

Normalization can be performed point-by-point as data arrives, e.g., thenormalization may be done point-by-point in near real time by a digitalprocessor, e.g., digital processor 430 shown in FIG. 4 . As describedabove in reference to FIGS. 6-8 , point-by-point normalization isbeneficial for signals with time-varying drifts because a differentnormalization factor can be determined for each data point, e.g.,normalization factor can change depending on the measured gain drift andthe baseline shift. Likewise, a new charge injection offset can also bedetermined for each cycle. Alternatively, each data point to benormalized can be an average of individual measurements in a cycle,e.g., an average of points over a time window.

As to filtering embodiments, it is possible to determine the baselineshift b and gain drift m through a deconvolution process that employsboth the bright mode data and dark mode data. However, if there is fastthreading, then there may not be many bright mode open channel datapoints and thus a reliable deconvolution cannot be performed. Forexample, fast threading might occur immediately when the bright modebegins. Then there would only be the threaded data and no open channeldata. Furthermore, it may take 100 cycles for the tag to be catalyzed tothe nucleic acid strand, and thus bright mode open channel data will bemissing for these 100 cycles (which amounts to more than is if missingbright mode data for an 80 Hz cycle frequency). Accordingly, gain driftand baseline shift could be difficult to track with just the threadedchannel. In some embodiments, the hybrid online normalization methodprovides an improved normalization technique when open channel data issimply not reliably available in the bright mode. As described above,the hybrid online technique leverages information from the dark mode toprovide a measurement of the open channel value in the bright mode,e.g., by leveraging the model of Eqns. (1) and (2) above.

In some embodiments, the hybrid online normalization technique can beemployed without using any additional filtering or signal processingtechniques (such as Kalman filtering or the like). In this case, Eqns.(1)-(2) can be solved using an estimate of P/P0 from a previous cycle,with the initial values of: P/P0=1, m=1, and b=0. Using these initialvalues and the new measurements of P(t) and N(t), Eqns. (1)-(2) can besolved to obtain the new estimate for the values measured at the cycle.However, such a solution just provides a comparison of the current openchannel value to the initial one and may be prone to error. Such asolution does not leverage the information from previous cycles todetermine a stable normalization factor. In addition, such a solutiondoes not leverage the correlation between the positive and negativechannels.

As an improved technique, it is beneficial to employ an estimationfilter (e.g. a Kalman filter or the like) that leverages historicalinformation (the measurement values themselves in addition to the theirrespective noise distributions) to determine an improved normalizationfactor at each cycle, while also not having to analyze all of the datacollectively. Accordingly, such a technique is beneficial because itfully leverages all available information to make more accurateestimates of the raw measured data and also does not require all of theraw data to be stored in memory, thereby loosening the hardwarerequirements for practical implementation. With respect to leveragingprior knowledge of the accuracy/noise of historical measurements, forexample, embodiments can determine values for b and m and also theirrespective confidence values. In addition, for each new data point, newupdated estimates for b and m and their respective new confidence valuescan be determined. When obtaining updated estimates for b and m, theconfidence values can then be used to indicate how much to weight thenew values for b and m when computing updated estimates, e.g., whencombining the new values with the previous value to obtain the updatedestimates that more closely match the ground truth.

Thus, in some embodiments, a current state (P, N, b and/or m) can bedetermined and, as the system evolves in time, new measurements of thestate can be made as well as new measurements of the uncertaintyassociated with the measurements of the new state. If the measurementsare not very certain, then the estimated value (i.e. the value outputfrom the estimation filter, e.g., the Kalman filter) of the new statewill be dominated by the value of the current state (i.e., whencombining the old and new measurements to get the estimate of the newstate, the weight for the new measurement will be small compared to theprevious measurement). Likewise, the updates of b and m can be performedseparately, with each having a current state (and measurement of thatcurrent state) and a new state (and a measurement of that new state). Insome embodiments, the new measurement can be determined by solving Eqns.(1)-(2). In some implementations, the uncertainty for the current statecan be determined from how m is changing, e.g., by determining avariance of m values. In other implementations, the uncertainty into thenew observation can be specified as a constant, empirical parameter(e.g., 0.03). Further for the current state, one can see how the newmeasurement is different than the current state, and that difference canbe used to determine the uncertainty over time. More measurements shouldprovide more certainty, as for standard error of the mean.

In some embodiments, a threading voltage could be used instead of or inaddition to N. The use of both open channel voltages and one or morethreaded voltages can provide an overdetermined system. One couldidentify a voltage range corresponding to a particular tag, so that onlyone positive channel is used. Another way is to have a weighting matrix,such that all threaded voltages are used, but the solution can beweighted toward the tag for which the voltage is expected to correspond.In one embodiment, if a threaded channel is observed and no positiveopen channel is observed, one can ignore the gain drift (e.g., set atthe previous value) and solve for b, e.g., using just the dark mode.Since the gain moves at a slower timeline, such a treatment can bejustified.

VI. Determining Bases

After normalization, embodiments can determine clusters of voltages forthe threaded channels, and use the clusters to determine cutoff voltagesfor discriminating between different bases. In some embodiments, aLaplacian mixture model can be used. The width for the Laplacian can bedetermined as part of the fitting procedure. There can be 5 Laplacianfunctions, one for positive open channel and one for each of the fournucleotides. The clusters can be determined per cell.

With a stable chip, the levels could be stable across cells and chips.Even so, monitoring can still be performed. The clustering can be doneat the end, or updated as new data is determined for a sequencing run.

The baseline shift b can be used in other contexts, e.g., in later partsof the pipeline. For example, information can be used about the signalthrough time, and information about each unique pore and sensor complex.For instance, b is function of t, as a second order signal of what wasthreaded and how long was it threaded for. This can be used forpolishing information for basecalling.

The normalization can be used to feed two estimates about thereliability of the base call. To get an estimate of the uncertainty fromthe Kalman filter, the uncertainty can be used to adjust the Q score.Thus, the uncertainty can be used as an input parameter to thedetermination of the Q score. The uncertainty can be viewed as how welldid the normalization work.

VII. Computer System

Any of the computer systems mentioned herein may utilize any suitablenumber of subsystems. Examples of such subsystems are shown in FIG. 20in computer system 2010. In some embodiments, a computer system includesa single computer apparatus, where the subsystems can be the componentsof the computer apparatus. In other embodiments, a computer system caninclude multiple computer apparatuses, each being a subsystem, withinternal components. A computer system can include desktop and laptopcomputers, tablets, mobile phones and other mobile devices.

The subsystems shown in FIG. 20 are interconnected via a system bus2075. Additional subsystems such as a printer 2074, keyboard 2078,storage device(s) 2079, monitor 2076, which is coupled to displayadapter 2082, and others are shown. Peripherals and input/output (I/O)devices, which couple to I/O controller 2071, can be connected to thecomputer system by any number of means known in the art such asinput/output (I/O) port 2077 (e.g., USB, FireWire®). For example, I/Oport 2077 or external interface 2081 (e.g. Ethernet, Wi-Fi, etc.) can beused to connect computer system 2010 to a wide area network such as theInternet, a mouse input device, or a scanner. The interconnection viasystem bus 2075 allows the central processor 2073 to communicate witheach subsystem and to control the execution of a plurality ofinstructions from system memory 2072 or the storage device(s) 2079(e.g., a fixed disk, such as a hard drive, or optical disk), as well asthe exchange of information between subsystems. The system memory 2072and/or the storage device(s) 2079 may embody a computer readable medium.Another subsystem is a data collection device 85, such as a camera,microphone, accelerometer, and the like. Any of the data mentionedherein can be output from one component to another component and can beoutput to the user.

A computer system can include a plurality of the same components orsubsystems, e.g., connected together by external interface 2081 or by aninternal interface. In some embodiments, computer systems, subsystem, orapparatuses can communicate over a network. In such instances, onecomputer can be considered a client and another computer a server, whereeach can be part of a same computer system. A client and a server caneach include multiple systems, subsystems, or components.

Aspects of embodiments can be implemented in the form of control logicusing hardware (e.g. an application specific integrated circuit or fieldprogrammable gate array) and/or using computer software with a generallyprogrammable processor in a modular or integrated manner. As usedherein, a processor includes a single-core processor, multi-coreprocessor on a same integrated chip, or multiple processing units on asingle circuit board or networked. Based on the disclosure and teachingsprovided herein, a person of ordinary skill in the art will know andappreciate other ways and/or methods to implement embodiments of thepresent invention using hardware and a combination of hardware andsoftware.

Any of the software components or functions described in thisapplication may be implemented as software code to be executed by aprocessor using any suitable computer language such as, for example,Java, C, C++, C#, Objective-C, Swift, or scripting language such as Perlor Python using, for example, conventional or object-orientedtechniques. The software code may be stored as a series of instructionsor commands on a computer readable medium for storage and/ortransmission. A suitable non-transitory computer readable medium caninclude random access memory (RAM), a read only memory (ROM), a magneticmedium such as a hard-drive or a floppy disk, or an optical medium suchas a compact disk (CD) or DVD (digital versatile disk), flash memory,and the like. The computer readable medium may be any combination ofsuch storage or transmission devices.

Such programs may also be encoded and transmitted using carrier signalsadapted for transmission via wired, optical, and/or wireless networksconforming to a variety of protocols, including the Internet. As such, acomputer readable medium may be created using a data signal encoded withsuch programs. Computer readable media encoded with the program code maybe packaged with a compatible device or provided separately from otherdevices (e.g., via Internet download). Any such computer readable mediummay reside on or within a single computer product (e.g. a hard drive, aCD, or an entire computer system), and may be present on or withindifferent computer products within a system or network. A computersystem may include a monitor, printer, or other suitable display forproviding any of the results mentioned herein to a user.

Any of the methods described herein may be totally or partiallyperformed with a computer system including one or more processors, whichcan be configured to perform the steps. Thus, embodiments can bedirected to computer systems configured to perform the steps of any ofthe methods described herein, potentially with different componentsperforming a respective steps or a respective group of steps. Althoughpresented as numbered steps, steps of methods herein can be performed ata same time or in a different order. Additionally, portions of thesesteps may be used with portions of other steps from other methods. Also,all or portions of a step may be optional. Additionally, any of thesteps of any of the methods can be performed with modules, units,circuits, or other means for performing these steps.

The specific details of particular embodiments may be combined in anysuitable manner without departing from the spirit and scope ofembodiments of the invention. However, other embodiments of theinvention may be directed to specific embodiments relating to eachindividual aspect, or specific combinations of these individual aspects.

The above description of example embodiments of the invention has beenpresented for the purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdescribed, and many modifications and variations are possible in lightof the teaching above.

A recitation of “a”, “an” or “the” is intended to mean “one or more”unless specifically indicated to the contrary. The use of “or” isintended to mean an “inclusive or,” and not an “exclusive or” unlessspecifically indicated to the contrary. Reference to a “first” componentdoes not necessarily require that a second component be provided.Moreover reference to a “first” or a “second” component does not limitthe referenced component to a particular location unless expresslystated.

All patents, patent applications, publications, and descriptionsmentioned herein are incorporated by reference in their entirety for allpurposes. None is admitted to be prior art.

What is claimed is:
 1. A system comprising: a plurality of sequencingcells, each sequencing cell comprising a nanopore; a signal generatorthat applies a signal across the nanopores of the plurality ofsequencing cells; and electrical circuitry coupled with the signalgenerator and the plurality of sequencing cells, the electricalcircuitry configured to: apply a first voltage across the nanopore ofeach sequencing cell while the nanopore is in an unthreaded state andmeasure an open channel voltage for each sequencing cell; after amolecule is inserted into nanopore of each sequencing cell so that thenanopore is in a threaded state, apply a second voltage across thenanopore of each sequencing cell while the nanopore is in the threadedstate and measure a first voltage signal for each sequencing cell;normalize the first voltage signal for each sequencing cell with thecorresponding open channel voltage for each sequencing cell; anddetermine an identity of at least a portion of the molecule based on thenormalized first voltage signal.
 2. The system of claim 1, wherein thefirst voltage and the second voltage have a same magnitude.
 3. Thesystem of claim 1, wherein the open channel voltage is determined usinga single point measurement of the open channel voltage for eachsequencing cell.
 4. The system of claim 1, wherein the open channelvoltage is determined using an average of more than one measurement ofthe open channel voltage for each sequencing cell.
 5. The system ofclaim 1, wherein the electrical circuitry is further configured to:identify a first subset of sequencing cells having only a singlenanopore and a second subset of sequencing cells having more than onenanopore based on a magnitude of the open channel voltage from eachsequencing cell.
 6. The system of claim 5, wherein the electricalcircuitry is further configured to: deactivate the second subset ofsequencing cells that have been identified as having more than onenanopore.
 7. The system of claim 5, wherein the electrical circuitry isfurther configured to: ignore data generated by the second subset ofsequencing cells that have been identified as having more than onenanopore.
 8. The system of claim 1, wherein the electrical circuitry isfurther configured to: for a sequencing cell of the plurality ofsequencing cells, estimate the open channel voltage at a time betweenmeasurements of the open channel voltage based on a gain drift; andnormalize the first voltage signal using the estimated open channelvoltage.
 9. The system of claim 1, wherein the electrical circuitry isfurther configured to: for a sequencing cell of the plurality ofsequencing cells, estimate the open channel voltage at a time betweenmeasurements of the open channel voltage based on a baseline shift; andnormalize the first voltage signal using the estimated open channelvoltage.
 10. The system of claim 1, wherein the electrical circuitry isfurther configured to: for a sequencing cell of the plurality ofsequencing cells, estimate the open channel voltage at a time betweenmeasurements of the open channel voltage based on both a gain drift anda baseline shift; and normalize the first voltage signal using theestimated open channel voltage.
 11. A method of calibrating a sequencingchip including a plurality of sequencing cells, each sequencing cellcomprising a nanopore, the method comprising: applying a first voltageacross the nanopore of each sequencing cell while the nanopore is in anunthreaded state and measuring an open channel voltage for eachsequencing cell; inserting a molecule into nanopore of each sequencingcell so that the nanopore is in a threaded state; applying a secondvoltage across the nanopore of each sequencing cell while the nanoporeis in the threaded state and measuring a first voltage signal for eachsequencing cell; normalizing the first voltage signal for eachsequencing cell with the corresponding open channel voltage for eachsequencing cell; and determining an identity of at least a portion ofthe molecule based on the normalized first voltage signal.
 12. Themethod of claim 11, wherein the first voltage and the second voltagehave a same magnitude.
 13. The method of claim 11, wherein the openchannel voltage is determined using a single point measurement of theopen channel voltage for each sequencing cell.
 14. The method of claim11, wherein the open channel voltage is determined using an average ofmore than one measurement of the open channel voltage for eachsequencing cell.
 15. The method of claim 11, further comprisingidentifying a first subset of sequencing cells having only a singlenanopore and a second subset of sequencing cells having more than onenanopore based on a magnitude of the open channel voltage from eachsequencing cell.
 16. The method of claim 15, further comprisingdeactivating the second subset of sequencing cells that have beenidentified as having more than one nanopore.
 17. The method of claim 15,further comprising ignoring data generated by the second subset ofsequencing cells that have been identified as having more than onenanopore.
 18. The method of claim 11, further comprising: for asequencing cell of the plurality of sequencing cells, estimating theopen channel voltage at a time between measurements of the open channelvoltage based on a gain drift; and normalizing the first voltage signalusing the estimated open channel voltage.
 19. The method of claim 11,further comprising: for a sequencing cell of the plurality of sequencingcells, estimating the open channel voltage at a time betweenmeasurements of the open channel voltage based on a baseline shift; andnormalizing the first voltage signal using the estimated open channelvoltage.
 20. The method of claim 11, further comprising: for asequencing cell of the plurality of sequencing cells, estimating theopen channel voltage at a time between measurements of the open channelvoltage based on both a gain drift and a baseline shift; and normalizingthe first voltage signal using the estimated open channel voltage.